1
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Chen B, Liu G, Chen Q, Wang H, Liu L, Tang K. Discovery of a novel marine Bacteroidetes with a rich repertoire of carbohydrate-active enzymes. Comput Struct Biotechnol J 2024; 23:406-416. [PMID: 38235362 PMCID: PMC10792170 DOI: 10.1016/j.csbj.2023.12.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 12/20/2023] [Accepted: 12/23/2023] [Indexed: 01/19/2024] Open
Abstract
Members of the phylum Bacteroidetes play a key role in the marine carbon cycle through their degradation of polysaccharides via carbohydrate-active enzymes (CAZymes) and polysaccharide utilization loci (PULs). The discovery of novel CAZymes and PULs is important for our understanding of the marine carbon cycle. In this study, we isolated and identified a potential new genus of the family Catalimonadaceae, in the phylum Bacteroidetes, from the southwest Indian Ocean. Strain TK19036, the type strain of the new genus, is predicted to encode CAZymes that are relatively abundant in marine Bacteroidetes genomes. Tunicatimonas pelagia NBRC 107804T, Porifericola rhodea NBRC 107748T and Catalinimonas niigatensis NBRC 109829T, which exhibit 16 S rRNA similarities exceeding 90% with strain TK19036, and belong to the same family, were selected as reference strains. These organisms possess a highly diverse repertoire of CAZymes and PULs, which may enable them to degrade a wide range of polysaccharides, especially pectin and alginate. In addition, some secretory CAZymes in strain TK19036 and its relatives were predicted to be transported by type IX secretion system (T9SS). Further, to the best of our knowledge, we propose the first reported "hybrid" PUL targeting alginates in T. pelagia NBRC 107804T. Our findings provide new insights into the polysaccharide degradation capacity of marine Bacteroidetes, and suggest that T9SS may play a more important role in this process than previously believed.
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Affiliation(s)
- Beihan Chen
- State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Science, Fujian Key Laboratory of Marine Carbon Sequestration, Xiamen University, Xiamen, China
- School of Oceanography, Shanghai Jiao Tong University, Shanghai, China
| | - Guohua Liu
- State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Science, Fujian Key Laboratory of Marine Carbon Sequestration, Xiamen University, Xiamen, China
| | - Quanrui Chen
- State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Science, Fujian Key Laboratory of Marine Carbon Sequestration, Xiamen University, Xiamen, China
| | - Huanyu Wang
- State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Science, Fujian Key Laboratory of Marine Carbon Sequestration, Xiamen University, Xiamen, China
| | - Le Liu
- State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Science, Fujian Key Laboratory of Marine Carbon Sequestration, Xiamen University, Xiamen, China
| | - Kai Tang
- State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Science, Fujian Key Laboratory of Marine Carbon Sequestration, Xiamen University, Xiamen, China
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2
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Tardelli S, Nizzoli L, Tesconi M, Conti M, Nakov P, Da San Martino G, Cresci S. Temporal dynamics of coordinated online behavior: Stability, archetypes, and influence. Proc Natl Acad Sci U S A 2024; 121:e2307038121. [PMID: 38709932 PMCID: PMC11098117 DOI: 10.1073/pnas.2307038121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 04/02/2024] [Indexed: 05/08/2024] Open
Abstract
Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out a dynamic analysis of coordinated behavior. To reach our goal, we build a multiplex temporal network and we perform dynamic community detection to identify groups of users that exhibited coordinated behaviors in time. We find that i) coordinated communities (CCs) feature variable degrees of temporal instability; ii) dynamic analyses are needed to account for such instability, and results of static analyses can be unreliable and scarcely representative of unstable communities; iii) some users exhibit distinct archetypal behaviors that have important practical implications; iv) content and network characteristics contribute to explaining why users leave and join CCs. Our results demonstrate the advantages of dynamic analyses and open up new directions of research on the unfolding of online debates, on the strategies of CCs, and on the patterns of online influence.
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Affiliation(s)
- Serena Tardelli
- Institute of Informatics and Telematics, National Research Council, Pisa56124, Italy
| | - Leonardo Nizzoli
- Institute of Informatics and Telematics, National Research Council, Pisa56124, Italy
| | - Maurizio Tesconi
- Institute of Informatics and Telematics, National Research Council, Pisa56124, Italy
| | - Mauro Conti
- Department of Mathematics, University of Padua, Padua35122, Italy
| | - Preslav Nakov
- Department of Natural Language Processing, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi54115, United Arab Emirates
| | | | - Stefano Cresci
- Institute of Informatics and Telematics, National Research Council, Pisa56124, Italy
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3
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Baqir A, Galeazzi A, Zollo F. News and misinformation consumption: A temporal comparison across European countries. PLoS One 2024; 19:e0302473. [PMID: 38717975 PMCID: PMC11078435 DOI: 10.1371/journal.pone.0302473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 04/04/2024] [Indexed: 05/12/2024] Open
Abstract
The Internet and social media have transformed the information landscape, democratizing content access and production. While making information easily accessible, these platforms can also act as channels for spreading misinformation, posing crucial societal challenges. To address this, understanding news consumption patterns and unraveling the complexities of the online information environment are essential. Previous studies highlight polarization and misinformation in online discussions, but many focus on specific topics or contexts, often overlooking comprehensive cross-country and cross-topic analyses. However, the dynamics of debates, misinformation prevalence, and the efficacy of countermeasures are intrinsically tied to socio-cultural contexts. This work aims to bridge this gap by exploring information consumption patterns across four European countries over three years. Analyzing the Twitter activity of news outlets in France, Germany, Italy, and the UK, this study seeks to shed light on how topics of European significance resonate across these nations and the role played by misinformation sources. The results spotlight that while reliable sources predominantly shape the information landscape, unreliable content persists across all countries and topics. Though most users favor trustworthy sources, a small percentage predominantly consumes content from questionable sources, with even fewer maintaining a mixed information diet. The cross-country comparison unravels disparities in audience overlap among news sources, the prevalence of misinformation, and the proportion of users relying on questionable sources. Such distinctions surface not only across countries but also within various topics. These insights underscore the pressing need for tailored studies, crucial in designing targeted and effective countermeasures against misinformation and extreme polarization in the digital space.
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Affiliation(s)
- Anees Baqir
- Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, Venice, Italy
| | - Alessandro Galeazzi
- Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, Venice, Italy
| | - Fabiana Zollo
- Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, Venice, Italy
- The New Institute Centre for Environmental Humanities, Venice, Italy
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4
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Darendeli A, Sun A, Tay WP. The geography of corporate fake news. PLoS One 2024; 19:e0301364. [PMID: 38630681 PMCID: PMC11023451 DOI: 10.1371/journal.pone.0301364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 03/08/2024] [Indexed: 04/19/2024] Open
Abstract
Although a rich academic literature examines the use of fake news by foreign actors for political manipulation, there is limited research on potential foreign intervention in capital markets. To address this gap, we construct a comprehensive database of (negative) fake news regarding U.S. firms by scraping prominent fact-checking sites. We identify the accounts that spread the news on Twitter (now X) and use machine-learning techniques to infer the geographic locations of these fake news spreaders. Our analysis reveals that corporate fake news is more likely than corporate non-fake news to be spread by foreign accounts. At the country level, corporate fake news is more likely to originate from African and Middle Eastern countries and tends to increase during periods of high geopolitical tension. At the firm level, firms operating in uncertain information environments and strategic industries are more likely to be targeted by foreign accounts. Overall, our findings provide initial evidence of foreign-originating misinformation in capital markets and thus have important policy implications.
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Affiliation(s)
- Alper Darendeli
- Nanyang Business School, Division of Accounting, Nanyang Technological University, Singapore, Singapore
| | - Aixin Sun
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Wee Peng Tay
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
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5
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Maertens R, Götz FM, Golino HF, Roozenbeek J, Schneider CR, Kyrychenko Y, Kerr JR, Stieger S, McClanahan WP, Drabot K, He J, van der Linden S. The Misinformation Susceptibility Test (MIST): A psychometrically validated measure of news veracity discernment. Behav Res Methods 2024; 56:1863-1899. [PMID: 37382812 PMCID: PMC10991074 DOI: 10.3758/s13428-023-02124-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/05/2023] [Indexed: 06/30/2023]
Abstract
Interest in the psychology of misinformation has exploded in recent years. Despite ample research, to date there is no validated framework to measure misinformation susceptibility. Therefore, we introduce Verification done, a nuanced interpretation schema and assessment tool that simultaneously considers Veracity discernment, and its distinct, measurable abilities (real/fake news detection), and biases (distrust/naïvité-negative/positive judgment bias). We then conduct three studies with seven independent samples (Ntotal = 8504) to show how to develop, validate, and apply the Misinformation Susceptibility Test (MIST). In Study 1 (N = 409) we use a neural network language model to generate items, and use three psychometric methods-factor analysis, item response theory, and exploratory graph analysis-to create the MIST-20 (20 items; completion time < 2 minutes), the MIST-16 (16 items; < 2 minutes), and the MIST-8 (8 items; < 1 minute). In Study 2 (N = 7674) we confirm the internal and predictive validity of the MIST in five national quota samples (US, UK), across 2 years, from three different sampling platforms-Respondi, CloudResearch, and Prolific. We also explore the MIST's nomological net and generate age-, region-, and country-specific norm tables. In Study 3 (N = 421) we demonstrate how the MIST-in conjunction with Verification done-can provide novel insights on existing psychological interventions, thereby advancing theory development. Finally, we outline the versatile implementations of the MIST as a screening tool, covariate, and intervention evaluation framework. As all methods are transparently reported and detailed, this work will allow other researchers to create similar scales or adapt them for any population of interest.
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Affiliation(s)
- Rakoen Maertens
- Department of Psychology, University of Cambridge, Downing Street, CB2 3EB, Cambridge, Cambridgeshire, UK.
| | - Friedrich M Götz
- Department of Psychology, University of British Columbia, 2136 West Mall, Vancouver, BC, V6T 1Z4, Canada
| | | | - Jon Roozenbeek
- Department of Psychology, University of Cambridge, Downing Street, CB2 3EB, Cambridge, Cambridgeshire, UK
| | - Claudia R Schneider
- Department of Psychology, University of Cambridge, Downing Street, CB2 3EB, Cambridge, Cambridgeshire, UK
| | - Yara Kyrychenko
- Department of Psychology, University of Cambridge, Downing Street, CB2 3EB, Cambridge, Cambridgeshire, UK
| | - John R Kerr
- Department of Psychology, University of Cambridge, Downing Street, CB2 3EB, Cambridge, Cambridgeshire, UK
| | - Stefan Stieger
- Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - William P McClanahan
- Department of Psychology, University of Cambridge, Downing Street, CB2 3EB, Cambridge, Cambridgeshire, UK
- Max Planck Institute for the Study of Crime, Security and Law, Freiburg im Breisgau, Germany
| | - Karly Drabot
- Department of Psychology, University of Cambridge, Downing Street, CB2 3EB, Cambridge, Cambridgeshire, UK
| | - James He
- Department of Psychology, University of Cambridge, Downing Street, CB2 3EB, Cambridge, Cambridgeshire, UK
| | - Sander van der Linden
- Department of Psychology, University of Cambridge, Downing Street, CB2 3EB, Cambridge, Cambridgeshire, UK
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6
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Zhang S, Hanjalic A, Wang H. Predicting nodal influence via local iterative metrics. Sci Rep 2024; 14:4929. [PMID: 38418506 PMCID: PMC10901818 DOI: 10.1038/s41598-024-55547-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 02/25/2024] [Indexed: 03/01/2024] Open
Abstract
Nodal spreading influence is the capability of a node to activate the rest of the network when it is the seed of spreading. Combining nodal properties (centrality metrics) derived from local and global topological information respectively has been shown to better predict nodal influence than using a single metric. In this work, we investigate to what extent local and global topological information around a node contributes to the prediction of nodal influence and whether relatively local information is sufficient for the prediction. We show that by leveraging the iterative process used to derive a classical nodal centrality such as eigenvector centrality, we can define an iterative metric set that progressively incorporates more global information around the node. We propose to predict nodal influence using an iterative metric set that consists of an iterative metric from order 1 to K produced in an iterative process, encoding gradually more global information as K increases. Three iterative metrics are considered, which converge to three classical node centrality metrics, respectively. In various real-world networks and synthetic networks with community structures, we find that the prediction quality of each iterative based model converges to its optimal when the metric of relatively low orders ( K ∼ 4 ) are included and increases only marginally when further increasing K. This fast convergence of prediction quality with K is further explained by analyzing the correlation between the iterative metric and nodal influence, the convergence rate of each iterative process and network properties. The prediction quality of the best performing iterative metric set with K = 4 is comparable with the benchmark method that combines seven centrality metrics: their prediction quality ratio is within the range [ 91 % , 106 % ] across all three quality measures and networks. In two spatially embedded networks with an extremely large diameter, however, iterative metric of higher orders, thus a large K, is needed to achieve comparable prediction quality with the benchmark.
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Affiliation(s)
- Shilun Zhang
- Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands
| | - Alan Hanjalic
- Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands
| | - Huijuan Wang
- Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands.
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7
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Quelle D, Bovet A. The perils and promises of fact-checking with large language models. Front Artif Intell 2024; 7:1341697. [PMID: 38384276 PMCID: PMC10879553 DOI: 10.3389/frai.2024.1341697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
Automated fact-checking, using machine learning to verify claims, has grown vital as misinformation spreads beyond human fact-checking capacity. Large language models (LLMs) like GPT-4 are increasingly trusted to write academic papers, lawsuits, and news articles and to verify information, emphasizing their role in discerning truth from falsehood and the importance of being able to verify their outputs. Understanding the capacities and limitations of LLMs in fact-checking tasks is therefore essential for ensuring the health of our information ecosystem. Here, we evaluate the use of LLM agents in fact-checking by having them phrase queries, retrieve contextual data, and make decisions. Importantly, in our framework, agents explain their reasoning and cite the relevant sources from the retrieved context. Our results show the enhanced prowess of LLMs when equipped with contextual information. GPT-4 outperforms GPT-3, but accuracy varies based on query language and claim veracity. While LLMs show promise in fact-checking, caution is essential due to inconsistent accuracy. Our investigation calls for further research, fostering a deeper comprehension of when agents succeed and when they fail.
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Affiliation(s)
- Dorian Quelle
- Department of Mathematical Modeling and Machine Learning, University of Zurich, Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
| | - Alexandre Bovet
- Department of Mathematical Modeling and Machine Learning, University of Zurich, Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
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8
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Oh J. The "Angry (Digital) Silver" in South Korea: The Rhetoric Around Older Adults' Digital Media Literacy. THE GERONTOLOGIST 2024; 64:gnad092. [PMID: 37439700 DOI: 10.1093/geront/gnad092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Indexed: 07/14/2023] Open
Abstract
Naïve yet threatening is how the South Korean news media have characterized some older adults who have taken to social media to air their political views. Labeled as "angry (digital) silver," these older adults using YouTube and other social media platforms for political activity are portrayed as digitally illiterate and aggressive. This paper examines the rhetoric surrounding older adults' digital media literacy in scholarship and popular news media with a focus on the news media's portrayal of older adults' digital political activity. By analyzing the use of language and various rhetorical strategies, I argue that specific rhetoric of caution, which warns against older adults' so-called lower digital media literacy, is used to invalidate their digital political activity. I draw upon the case of the "Taegukgi squad"-a political group mainly composed of older adults in South Korea-and the evolution of their digital presence. Addressing the media's biased portrayal of older adults' digital media literacy, this paper further invites reflection on controversies around the role of age in digital political activities around the globe.
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Affiliation(s)
- June Oh
- Department of Literature and Languages, University of Texas at Tyler, Tyler, Texas, USA
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9
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Hobbs A, Aldosery A, Kostkova P. Low credibility URL sharing on Twitter during reporting linking rare blood clots with the Oxford/AstraZeneca COVID-19 vaccine. PLoS One 2024; 19:e0296444. [PMID: 38241268 PMCID: PMC10798519 DOI: 10.1371/journal.pone.0296444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 11/23/2023] [Indexed: 01/21/2024] Open
Abstract
The COVID-19 pandemic was accompanied by an "infodemic" of misinformation. Misleading narratives around the virus, its origin, and treatments have had serious implications for public health. In March 2021, concerns were raised about links between the Oxford/AstraZeneca (AZ) COVID-19 vaccine and recipients developing blood clots. This paper aims to identify whether this prompted any reaction in the diffusion of low-credibility COVID-19-relate information on Twitter. Twitter's application programming interface was used to collect data containing COVID-19-related keywords between 4th and 25th March 2021, a period centred on the peak of new coverage linking rare blood clots with the AZ vaccine. We analysed and visualised the data using temporal analysis and social network analysis tools. We subsequently analysed the data to determine the most influential users and domains in the propagation of low-credibility information about COVID-19 and the AZ vaccine. This research presents evidence that the peak of news coverage linking rare blood clots with the AZ vaccine correlated with an increased volume and proportion of low-credibility AZ-related content propagated on Twitter. However, no equivalent changes to the volume, propagation, or network structure for the full dataset of COVID-19-related information or misinformation were observed. The research identified RT.com as the most prolific creator of low-credibility COVID-19-related content. It also highlighted the crucial role of self-promotion in the successful propagation of low-credibility content on Twitter. The findings suggest that the simple approach adopted within the research to identify the most popular and influential sources of low-credibility content presents a valuable opportunity for public health authorities and social media platforms to develop bespoke strategies to counter the propagation of misinformation in the aftermath of a breaking news event.
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Affiliation(s)
- Ali Hobbs
- IRDR Centre for Digital Public Health in Emergencies (dPHE), University College London, London, United Kingdom
| | - Aisha Aldosery
- IRDR Centre for Digital Public Health in Emergencies (dPHE), University College London, London, United Kingdom
| | - Patty Kostkova
- IRDR Centre for Digital Public Health in Emergencies (dPHE), University College London, London, United Kingdom
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10
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Haupt MR, Chiu M, Chang J, Li Z, Cuomo R, Mackey TK. Detecting nuance in conspiracy discourse: Advancing methods in infodemiology and communication science with machine learning and qualitative content coding. PLoS One 2023; 18:e0295414. [PMID: 38117843 PMCID: PMC10732406 DOI: 10.1371/journal.pone.0295414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/21/2023] [Indexed: 12/22/2023] Open
Abstract
The spread of misinformation and conspiracies has been an ongoing issue since the early stages of the internet era, resulting in the emergence of the field of infodemiology (i.e., information epidemiology), which investigates the transmission of health-related information. Due to the high volume of online misinformation in recent years, there is a need to continue advancing methodologies in order to effectively identify narratives and themes. While machine learning models can be used to detect misinformation and conspiracies, these models are limited in their generalizability to other datasets and misinformation phenomenon, and are often unable to detect implicit meanings in text that require contextual knowledge. To rapidly detect evolving conspiracist narratives within high volume online discourse while identifying nuanced themes requiring the comprehension of subtext, this study describes a hybrid methodology that combines natural language processing (i.e., topic modeling and sentiment analysis) with qualitative content coding approaches to characterize conspiracy discourse related to 5G wireless technology and COVID-19 on Twitter (currently known as 'X'). Discourse that focused on correcting 5G conspiracies was also analyzed for comparison. Sentiment analysis shows that conspiracy-related discourse was more likely to use language that was analytic, combative, past-oriented, referenced social status, and expressed negative emotions. Corrections discourse was more likely to use words reflecting cognitive processes, prosocial relations, health-related consequences, and future-oriented language. Inductive coding characterized conspiracist narratives related to global elites, anti-vax sentiment, medical authorities, religious figures, and false correlations between technology advancements and disease outbreaks. Further, the corrections discourse did not address many of the narratives prevalent in conspiracy conversations. This paper aims to further bridge the gap between computational and qualitative methodologies by demonstrating how both approaches can be used in tandem to emphasize the positive aspects of each methodology while minimizing their respective drawbacks.
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Affiliation(s)
- Michael Robert Haupt
- Department of Cognitive Science, University of California San Diego, La Jolla, California, United States of America
- Global Health Policy & Data Institute, San Diego, California, United States of America
| | - Michelle Chiu
- Department of Psychology, Temple University, Philadelphia, Pennsylvania, United States of America
| | - Joseline Chang
- Rady School of Management, University of California San Diego, La Jolla, California, United States of America
| | - Zoe Li
- Global Health Policy & Data Institute, San Diego, California, United States of America
- S-3 Research, San Diego, California, United States of America
| | - Raphael Cuomo
- Department of Anesthesiology, University of California, San Diego School of Medicine, San Diego, California, United States of America
| | - Tim K. Mackey
- S-3 Research, San Diego, California, United States of America
- Global Health Program, Department of Anthropology, University of California, San Diego, California, United States of America
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11
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Mekacher A, Falkenberg M, Baronchelli A. The systemic impact of deplatforming on social media. PNAS NEXUS 2023; 2:pgad346. [PMID: 37954163 PMCID: PMC10638500 DOI: 10.1093/pnasnexus/pgad346] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 10/06/2023] [Indexed: 11/14/2023]
Abstract
Deplatforming, or banning malicious accounts from social media, is a key tool for moderating online harms. However, the consequences of deplatforming for the wider social media ecosystem have been largely overlooked so far, due to the difficulty of tracking banned users. Here, we address this gap by studying the ban-induced platform migration from Twitter to Gettr. With a matched dataset of 15M Gettr posts and 12M Twitter tweets, we show that users active on both platforms post similar content as users active on Gettr but banned from Twitter, but the latter have higher retention and are 5 times more active. Our results suggest that increased Gettr use is not associated with a substantial increase in user toxicity over time. In fact, we reveal that matched users are more toxic on Twitter, where they can engage in abusive cross-ideological interactions, than Gettr. Our analysis shows that the matched cohort are ideologically aligned with the far-right, and that the ability to interact with political opponents may be part of Twitter's appeal to these users. Finally, we identify structural changes in the Gettr network preceding the 2023 Brasília insurrections, highlighting the risks that poorly regulated social media platforms may pose to democratic life.
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Affiliation(s)
- Amin Mekacher
- Department of Mathematics, City University of London, London EC1V 0HB, UK
| | - Max Falkenberg
- Department of Mathematics, City University of London, London EC1V 0HB, UK
| | - Andrea Baronchelli
- Department of Mathematics, City University of London, London EC1V 0HB, UK
- The Alan Turing Institute, British Library, London NW1 2DB, UK
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12
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Pierri F, Luceri L, Chen E, Ferrara E. How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events. EPJ DATA SCIENCE 2023; 12:43. [PMID: 37810187 PMCID: PMC10550859 DOI: 10.1140/epjds/s13688-023-00420-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 09/20/2023] [Indexed: 10/10/2023]
Abstract
Social media moderation policies are often at the center of public debate, and their implementation and enactment are sometimes surrounded by a veil of mystery. Unsurprisingly, due to limited platform transparency and data access, relatively little research has been devoted to characterizing moderation dynamics, especially in the context of controversial events and the platform activity associated with them. Here, we study the dynamics of account creation and suspension on Twitter during two global political events: Russia's invasion of Ukraine and the 2022 French Presidential election. Leveraging a large-scale dataset of 270M tweets shared by 16M users in multiple languages over several months, we identify peaks of suspicious account creation and suspension, and we characterize behaviors that more frequently lead to account suspension. We show how large numbers of accounts get suspended within days of their creation. Suspended accounts tend to mostly interact with legitimate users, as opposed to other suspicious accounts, making unwarranted and excessive use of reply and mention features, and sharing large amounts of spam and harmful content. While we are only able to speculate about the specific causes leading to a given account suspension, our findings contribute to shedding light on patterns of platform abuse and subsequent moderation during major events.
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Affiliation(s)
- Francesco Pierri
- Information Sciences Institute, University of Southern California, Los Angeles, USA
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Luca Luceri
- Information Sciences Institute, University of Southern California, Los Angeles, USA
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland
| | - Emily Chen
- Information Sciences Institute, University of Southern California, Los Angeles, USA
- Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, USA
| | - Emilio Ferrara
- Information Sciences Institute, University of Southern California, Los Angeles, USA
- Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, USA
- Annenberg School of Communication and Journalism, University of Southern California, Los Angeles, USA
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13
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Ferri I, Gaya-Àvila A, Díaz-Guilera A. Three-state opinion model with mobile agents. CHAOS (WOODBURY, N.Y.) 2023; 33:093121. [PMID: 37712914 DOI: 10.1063/5.0152674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 08/21/2023] [Indexed: 09/16/2023]
Abstract
We study an agent-based opinion model with two extreme (opposite) opinion states and a neutral intermediate one. We adjust the relative degree of conviction between extremists and neutrals through a dimensionless parameter called the "neutrality parameter" to investigate its impact on the outcome of the system. In our model, agents move randomly on a plane with periodic boundary conditions and interact with each other only when they are within a fixed distance threshold. We examine different movement mechanisms and their interplay with the neutrality parameter. Our results show that in general, mobility promotes the global consensus, especially for extreme opinions. However, it takes significantly less time to reach a consensus on the neutral opinion.
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Affiliation(s)
- I Ferri
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, 08028 Barcelona, Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, 08028 Barcelona, Spain
| | - A Gaya-Àvila
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, 08028 Barcelona, Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, 08028 Barcelona, Spain
| | - A Díaz-Guilera
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, 08028 Barcelona, Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, 08028 Barcelona, Spain
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14
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Ellis JT, Reichel MP. Twitter trends in #Parasitology determined by text mining and topic modelling. CURRENT RESEARCH IN PARASITOLOGY & VECTOR-BORNE DISEASES 2023; 4:100138. [PMID: 37670843 PMCID: PMC10475476 DOI: 10.1016/j.crpvbd.2023.100138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 09/07/2023]
Abstract
This study investigated the emergence and use of Twitter, as of July 2023 being rebranded as X, as the main forum for social media communication in parasitology. A dataset of tweets was constructed using a keyword search of Twitter with the search terms 'malaria', 'Plasmodium', 'Leishmania', 'Trypanosoma', 'Toxoplasma' and 'Schistosoma' for the period from 2011 to 2020. Exploratory data analyses of tweet content were conducted, including language, usernames and hashtags. To identify parasitology topics of discussion, keywords and phrases were extracted using KeyBert and biterm topic modelling. The sentiment of tweets was analysed using VADER. The results show that the number of tweets including the keywords increased from 2011 (for malaria) and 2013 (for the others) to 2020, with the highest number of tweets being recorded in 2020. The maximum number of yearly tweets for Plasmodium, Leishmania, Toxoplasma, Trypanosoma and Schistosoma was recorded in 2020 (2804, 2161, 1570, 680 and 360 tweets, respectively). English was the most commonly used language for tweeting, although the percentage varied across the searches. In tweets mentioning Leishmania, only ∼37% were in English, with Spanish being more common. Across all the searches, Portuguese was another common language found. Popular tweets on Toxoplasma contained keywords relating to mental health including depression, anxiety and schizophrenia. The Trypanosoma tweets referenced drugs (benznidazole, nifurtimox) and vectors (bugs, triatomines, tsetse), while the Schistosoma tweets referenced areas of biology including pathology, eggs and snails. A wide variety of individuals and organisations were shown to be associated with Twitter activity. Many journals in the parasitology arena regularly tweet about publications from their journal, and professional societies promote activity and events that are important to them. These represent examples of trusted sources of information, often by experts in their fields. Social media activity of influencers, however, who have large numbers of followers, might have little or no training in science. The existence of such tweeters does raise cause for concern to parasitology, as one may start to question the quality of information being disseminated.
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Affiliation(s)
- John T. Ellis
- School of Life Sciences, University of Technology Sydney, Broadway, NSW, Australia
| | - Michael P. Reichel
- Department of Population Medicine & Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
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15
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Albuquerque UP, Cantalice AS, Oliveira ES, de Moura JMB, dos Santos RKS, da Silva RH, Brito-Júnior VM, Ferreira-Júnior WS. Exploring Large Digital Bodies for the Study of Human Behavior. EVOLUTIONARY PSYCHOLOGICAL SCIENCE 2023; 9:1-10. [PMID: 37362224 PMCID: PMC10203656 DOI: 10.1007/s40806-023-00363-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/03/2023] [Accepted: 04/04/2023] [Indexed: 06/28/2023]
Abstract
Internet access has become a fundamental component of contemporary society, with major impacts in many areas that offer opportunities for new research insights. The search and deposition of information in digital media form large sets of data known as digital corpora, which can be used to generate structured data, representing repositories of knowledge and evidence of human culture. This information offers opportunities for scientific investigations that contribute to the understanding of human behavior on a large scale, reaching human populations/individuals that would normally be difficult to access. These tools can help access social and cultural varieties worldwide. In this article, we briefly review the potential of these corpora in the study of human behavior. Therefore, we propose Culturomics of Human Behavior as an approach to understand, explain, and predict human behavior using digital corpora.
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Affiliation(s)
- Ulysses Paulino Albuquerque
- Laboratório de Ecologia e Evolução de Sistemas Socioecológicos (LEA), Departamento de Botânica, Universidade Federal de Pernambuco, Av. Prof. Moraes Rego, Cidade Universitária, 123550670-901 Recife, Pernambuco, Brazil
| | - Anibal Silva Cantalice
- Laboratório de Ecologia e Evolução de Sistemas Socioecológicos (LEA), Departamento de Botânica, Universidade Federal de Pernambuco, Av. Prof. Moraes Rego, Cidade Universitária, 123550670-901 Recife, Pernambuco, Brazil
| | - Edwine Soares Oliveira
- Laboratório de Ecologia e Evolução de Sistemas Socioecológicos (LEA), Departamento de Botânica, Universidade Federal de Pernambuco, Av. Prof. Moraes Rego, Cidade Universitária, 123550670-901 Recife, Pernambuco, Brazil
| | - Joelson Moreno Brito de Moura
- Instituto de Estudos do Xingu (IEX), Av. Norte Sul, Universidade Federal do Sul E Sudeste do Pará, Loteamento Cidade Nova, Lote N. 1, Qd 15, Setor 15, São Félix Do Xingu, Brazil
| | - Rayane Karoline Silva dos Santos
- Laboratório de Ecologia e Evolução de Sistemas Socioecológicos (LEA), Departamento de Botânica, Universidade Federal de Pernambuco, Av. Prof. Moraes Rego, Cidade Universitária, 123550670-901 Recife, Pernambuco, Brazil
| | - Risoneide Henriques da Silva
- Laboratório de Ecologia e Evolução de Sistemas Socioecológicos (LEA), Departamento de Botânica, Universidade Federal de Pernambuco, Av. Prof. Moraes Rego, Cidade Universitária, 123550670-901 Recife, Pernambuco, Brazil
| | - Valdir Moura Brito-Júnior
- Laboratório de Ecologia e Evolução de Sistemas Socioecológicos (LEA), Departamento de Botânica, Universidade Federal de Pernambuco, Av. Prof. Moraes Rego, Cidade Universitária, 123550670-901 Recife, Pernambuco, Brazil
| | - Washington Soares Ferreira-Júnior
- Laboratório de Investigações Bioculturais no Semiárido, Universidade de Pernambuco, Campus Petrolina, BR203, Km 2, S/N, 56328-903 Petrolina, Pernambuco, Brazil
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16
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Phan HT, Nguyen NT, Hwang D. Fake news detection: A survey of graph neural network methods. Appl Soft Comput 2023; 139:110235. [PMID: 36999094 PMCID: PMC10036155 DOI: 10.1016/j.asoc.2023.110235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 08/03/2022] [Accepted: 03/19/2023] [Indexed: 04/01/2023]
Abstract
The emergence of various social networks has generated vast volumes of data. Efficient methods for capturing, distinguishing, and filtering real and fake news are becoming increasingly important, especially after the outbreak of the COVID-19 pandemic. This study conducts a multiaspect and systematic review of the current state and challenges of graph neural networks (GNNs) for fake news detection systems and outlines a comprehensive approach to implementing fake news detection systems using GNNs. Furthermore, advanced GNN-based techniques for implementing pragmatic fake news detection systems are discussed from multiple perspectives. First, we introduce the background and overview related to fake news, fake news detection, and GNNs. Second, we provide a GNN taxonomy-based fake news detection taxonomy and review and highlight models in categories. Subsequently, we compare critical ideas, advantages, and disadvantages of the methods in categories. Next, we discuss the possible challenges of fake news detection and GNNs. Finally, we present several open issues in this area and discuss potential directions for future research. We believe that this review can be utilized by systems practitioners and newcomers in surmounting current impediments and navigating future situations by deploying a fake news detection system using GNNs.
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Affiliation(s)
- Huyen Trang Phan
- Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea
- Faculty of Information Technology, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam
| | - Ngoc Thanh Nguyen
- Department of Applied Informatics, Wroclaw University of Science and Technology, Wroclaw, Poland
| | - Dosam Hwang
- Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea
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17
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ISE-Hate: A benchmark corpus for inter-faith, sectarian, and ethnic hatred detection on social media in Urdu. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2023.103270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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18
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Clemm von Hohenberg B. Truth and Bias, Left and Right: Testing Ideological Asymmetries with a Realistic News Supply. PUBLIC OPINION QUARTERLY 2023; 87:267-292. [PMID: 37502105 PMCID: PMC10371040 DOI: 10.1093/poq/nfad013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
The debate around "fake news" has raised the question of whether liberals and conservatives differ, first, in their ability to discern true from false information, and second, in their tendency to give more credit to information that is ideologically congruent. Typical designs to measure these asymmetries select, often arbitrarily, a small set of news items as experimental stimuli without clear reference to a "population of information." This pre-registered study takes an alternative approach by, first, conceptualizing estimands in relation to all political news. Second, to represent this target population, it uses a set of 80 randomly sampled items from a large collection of articles from Google News and three fact-checking sites. In a subsequent survey, a quota sample of US participants (n = 1,393) indicate whether they believe the news items to be true. Conservatives are less truth-discerning than liberals, but also less affected by the congruence of news.
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Affiliation(s)
- Bernhard Clemm von Hohenberg
- Corresponding author: Bernhard Clemm von Hohenberg, Department of Computational Social Sciences, GESIS Leibniz Institute for the Social Sciences, Unter Sachsenhausen 6-8, D-50667 Cologne, Germany;
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19
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Patuelli A, Saracco F. Sustainable development goals as unifying narratives in large UK firms' Twitter discussions. Sci Rep 2023; 13:7017. [PMID: 37120611 PMCID: PMC10148845 DOI: 10.1038/s41598-023-34024-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 04/22/2023] [Indexed: 05/01/2023] Open
Abstract
To achieve sustainable development worldwide, the United Nations set 17 Sustainable Development Goals (SDGs) for humanity to reach by 2030. Society is involved in the challenge, with firms playing a crucial role. Thus, a key question is to what extent firms engage with the SDGs. Efforts to map firms' contributions have mainly focused on analysing companies' reports based on limited samples and non-real-time data. We present a novel interdisciplinary approach based on analysing big data from an online social network (Twitter) with complex network methods from statistical physics. By doing so, we provide a comprehensive and nearly real-time picture of firms' engagement with SDGs. Results show that: (1) SDGs themes tie conversations among major UK firms together; (2) the social dimension is predominant; (3) the attention to different SDGs themes varies depending on the community and sector firms belong to; (4) stakeholder engagement is higher on posts related to global challenges compared to general ones; (5) large UK companies and stakeholders generally behave differently from Italian ones. This paper provides theoretical contributions and practical implications relevant to firms, policymakers and management education. Most importantly, it provides a novel tool and a set of keywords to monitor the influence of the private sector on the implementation of the 2030 Agenda.
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Affiliation(s)
- Alessia Patuelli
- Northumbria University, Amsterdam Campus, Corry Tendeloo Building (CTH), Fraijlemaborg 133, 1102CV, Amsterdam, The Netherlands.
- IMT School for Advanced Studies Lucca, p.zza S. Francesco 19, 55100, Lucca, Italy.
- Department of Economics and Management, University of Florence, 50127, Florence, Italy.
| | - Fabio Saracco
- IMT School for Advanced Studies Lucca, p.zza S. Francesco 19, 55100, Lucca, Italy
- Institute for Applied Computing, National Research Council of Italy, via dei Taurini 19, 00185, Rome, Italy
- Enrico Fermi' Research Center (CREF), via Panisperna 89A, 00184, Rome, Italy
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20
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Schieber TA, Carpi LC, Pardalos PM, Masoller C, Díaz-Guilera A, Ravetti MG. Diffusion capacity of single and interconnected networks. Nat Commun 2023; 14:2217. [PMID: 37072418 PMCID: PMC10113202 DOI: 10.1038/s41467-023-37323-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 03/10/2023] [Indexed: 04/20/2023] Open
Abstract
Understanding diffusive processes in networks is a significant challenge in complexity science. Networks possess a diffusive potential that depends on their topological configuration, but diffusion also relies on the process and initial conditions. This article presents Diffusion Capacity, a concept that measures a node's potential to diffuse information based on a distance distribution that considers both geodesic and weighted shortest paths and dynamical features of the diffusion process. Diffusion Capacity thoroughly describes the role of individual nodes during a diffusion process and can identify structural modifications that may improve diffusion mechanisms. The article defines Diffusion Capacity for interconnected networks and introduces Relative Gain, which compares the performance of a node in a single structure versus an interconnected one. The method applies to a global climate network constructed from surface air temperature data, revealing a significant change in diffusion capacity around the year 2000, suggesting a loss of the planet's diffusion capacity that could contribute to the emergence of more frequent climatic events.
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Affiliation(s)
- Tiago A Schieber
- Departamento de Ciências Administrativas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Laura C Carpi
- Instituto Nacional de Ciência e Tecnologia, Sistemas Complexos, INCT-SC, CEFET-MG, Belo Horizonte, MG, Brazil
- Machine Intelligence and Data Science Laboratory (MINDS), Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Panos M Pardalos
- Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA
- Lab LATNA, National Research University, Higher School of Economics, Nizhny Novgorod, Russia
| | - Cristina Masoller
- Departament de Física, Universitat Politècnica de Catalunya, Terrassa, BCN, Spain
| | - Albert Díaz-Guilera
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona, BCN, Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Barcelona, BCN, Spain
| | - Martín G Ravetti
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
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21
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Gao W, Ni M, Deng H, Zhu X, Zeng P, Hu X. Few-shot fake news detection via prompt-based tuning. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-221647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
As people increasingly use social media to read news, fake news has become a major problem for the public and government. One of the main challenges in fake news detection is how to identify them in the early stage of propagation. Another challenge is that detection model training requires large amounts of labeled data, which are often unavailable or expensive to acquire. To address these challenges, we propose a novel Fake News Detection model based on Prompt Tuning (FNDPT). FNDPT first designs a prompt-based template for early fake news detection. This mechanism incorporates contextual information into textual content and extracts relevant knowledge from pre-trained language models. Furthermore, our model utilizes prompt-based tuning to enhance the performance in a few-shot setting. Experimental results on two real-world datasets verify the effectiveness of FNDPT.
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Affiliation(s)
- Wang Gao
- School of Artificial Intelligence, Jianghan University, Wuhan, China
| | - Mingyuan Ni
- School of Artificial Intelligence, Jianghan University, Wuhan, China
| | - Hongtao Deng
- School of Artificial Intelligence, Jianghan University, Wuhan, China
| | - Xun Zhu
- School of Artificial Intelligence, Jianghan University, Wuhan, China
| | - Peng Zeng
- School of Artificial Intelligence, Jianghan University, Wuhan, China
| | - Xi Hu
- School of Artificial Intelligence, Jianghan University, Wuhan, China
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22
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Sharma R, Arya A. LFWE:
L
inguistic
F
eature Based
W
ord
E
mbedding for Hindi Fake News Detection. ACM T ASIAN LOW-RESO 2023. [DOI: 10.1145/3589764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
It is essential for the research communities to investigate ways for authenticating news. The use of linguistic feature-based analysis to automatically detect false news is gaining popularity among the scientific community. However, such techniques are exclusively created for English, leaving low-resource languages, like Hindi behind. To address this issue, we constructed a novel annotated Hindi Fake News (HinFakeNews) dataset of roughly 33,300 articles that can be utilized to develop autonomous fake news detection systems. This work provides a two-stage benchmark model for identifying fake news in Hindi using machine learning. The proposed model, Linguistic Feature Based Word Embedding (LFWE) generates Word Embedding (WE) over linguistic features. This paper focuses on 24 key linguistic features (14 extracted and 10 derived) for successful detection of Hindi fake news. These features are grouped as lexical, semantic, syntactic, psycholinguistic, readability, and quantity features. The contribution is two-fold: In the first phase, the dataset is pre-processed and linguistic features are extracted. In the second phase, Feature Sets (F-Sets) are generated as WE, and an Ensemble voting classification is carried out on the F-Sets. According to experimental findings, the LFWE model accurately detects and classifies fake news in Hindi with an accuracy of 98.49%.
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23
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Shi B, Xu K, Zhao J. The long-term impacts of air quality on fine-grained online emotional responses to haze pollution in 160 Chinese cities. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 864:161160. [PMID: 36572304 DOI: 10.1016/j.scitotenv.2022.161160] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 12/17/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Air pollution poses a great threat to public health and social stability by influencing multiple emotions. In particular, the air quality in developing countries is deteriorating along with rapid industrialization and urbanization, and multiple emotions may change along with regulation updates and air quality trending. Monitoring changes in public emotion is crucial for environmental governance. However, limited evidence exists for long-term effects of air quality on fine-grained emotions. Traditional surveys have the drawbacks of spatial limitations and high costs of time and money. Here, we use deep learning models to identify multiple emotions of over 10 million haze-related tweets and evaluate the effect of air quality on emotional predispositions for 160 cities from 2014 to 2019 in China. We find that sadness and joy are persistently associated with air quality, while anger and disgust are not. Surprisingly, the effects on fear vanished in the last three years. Moreover, air pollution initially had a greater impact on expressed fear in cities with higher income, poorer air quality and a greater percentage of women. Through popularity ranking and dynamic topic model, we interpretively revealed that people are no longer overly panicked and their attention is shifting toward policies and sources of haze. Our findings highlight the temporal evolution in the public's emotional response and provide significant implications for equitable public policies.
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Affiliation(s)
- Bowen Shi
- State Key Laboratory of Software Development Environment, Beihang University, Beijing 10091, China
| | - Ke Xu
- State Key Laboratory of Software Development Environment, Beihang University, Beijing 10091, China
| | - Jichang Zhao
- School of Economics and Management, Beihang University, Beijing 10091, China.
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24
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Flamino J, Galeazzi A, Feldman S, Macy MW, Cross B, Zhou Z, Serafino M, Bovet A, Makse HA, Szymanski BK. Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections. Nat Hum Behav 2023:10.1038/s41562-023-01550-8. [PMID: 36914806 DOI: 10.1038/s41562-023-01550-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 02/03/2023] [Indexed: 03/16/2023]
Abstract
Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter's news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers-users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels.
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Affiliation(s)
- James Flamino
- Department of Computer Science and Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Alessandro Galeazzi
- University of Brescia, Brescia, Italy.,Ca' Foscari University of Venice, Venice, Italy
| | | | - Michael W Macy
- Departments of Information Science and Sociology, Cornell University, Ithaca, NY, USA
| | - Brendan Cross
- Department of Computer Science and Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Zhenkun Zhou
- School of Statistics, Capital University of Economics and Business, Beijing, China
| | - Matteo Serafino
- Levich Institute and Physics Department, City College of New York, New York, NY, USA
| | - Alexandre Bovet
- Department of Mathematics and Digital Society Initiative, University of Zurich, Zurich, Switzerland
| | - Hernán A Makse
- Levich Institute and Physics Department, City College of New York, New York, NY, USA.
| | - Boleslaw K Szymanski
- Department of Computer Science and Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY, USA.
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25
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Ahmed S, Khan DM, Sadiq S, Umer M, Shahzad F, Mahmood K, Mohsen H, Ashraf I. Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques. PeerJ Comput Sci 2023; 9:e1190. [PMID: 37346678 PMCID: PMC10280254 DOI: 10.7717/peerj-cs.1190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/28/2022] [Indexed: 06/23/2023]
Abstract
The outbreak of the COVID-19 pandemic has also triggered a tsunami of news, instructions, and precautionary measures related to the disease on social media platforms. Despite the considerable support on social media, a large number of fake propaganda and conspiracies are also circulated. People also reacted to COVID-19 vaccination on social media and expressed their opinions, perceptions, and conceptions. The present research work aims to explore the opinion dynamics of the general public about COVID-19 vaccination to help the administration authorities to devise policies to increase vaccination acceptance. For this purpose, a framework is proposed to perform sentiment analysis of COVID-19 vaccination-related tweets. The influence of term frequency-inverse document frequency, bag of words (BoW), Word2Vec, and combination of TF-IDF and BoW are explored with classifiers including random forest, gradient boosting machine, extra tree classifier (ETC), logistic regression, Naïve Bayes, stochastic gradient descent, multilayer perceptron, convolutional neural network (CNN), bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and recurrent neural network (RNN). Results reveal that ETC outperforms using BoW with a 92% of accuracy and is the most suitable approach for sentiment analysis of COVID-19-related tweets. Opinion dynamics show that sentiments in favor of vaccination have increased over time.
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Affiliation(s)
- Shoaib Ahmed
- Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Dost Muhammad Khan
- Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Saima Sadiq
- Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Muhammad Umer
- Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Faisal Shahzad
- Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | | | - Heba Mohsen
- Computer Science Department, Future University in Egypt, New Cairo, Egypt
| | - Imran Ashraf
- Information and Communication Engineering, Yeungnam University, Gyeongsan si, South Korea
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26
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Cai M, Luo H, Meng X, Cui Y, Wang W. Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2022.103197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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27
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Pierri F, DeVerna MR, Yang KC, Axelrod D, Bryden J, Menczer F. One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study. J Med Internet Res 2023; 25:e42227. [PMID: 36735835 PMCID: PMC9970010 DOI: 10.2196/42227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 01/18/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Vaccinations play a critical role in mitigating the impact of COVID-19 and other diseases. Past research has linked misinformation to increased hesitancy and lower vaccination rates. Gaps remain in our knowledge about the main drivers of vaccine misinformation on social media and effective ways to intervene. OBJECTIVE Our longitudinal study had two primary objectives: (1) to investigate the patterns of prevalence and contagion of COVID-19 vaccine misinformation on Twitter in 2021, and (2) to identify the main spreaders of vaccine misinformation. Given our initial results, we further considered the likely drivers of misinformation and its spread, providing insights for potential interventions. METHODS We collected almost 300 million English-language tweets related to COVID-19 vaccines using a list of over 80 relevant keywords over a period of 12 months. We then extracted and labeled news articles at the source level based on third-party lists of low-credibility and mainstream news sources, and measured the prevalence of different kinds of information. We also considered suspicious YouTube videos shared on Twitter. We focused our analysis of vaccine misinformation spreaders on verified and automated Twitter accounts. RESULTS Our findings showed a relatively low prevalence of low-credibility information compared to the entirety of mainstream news. However, the most popular low-credibility sources had reshare volumes comparable to those of many mainstream sources, and had larger volumes than those of authoritative sources such as the US Centers for Disease Control and Prevention and the World Health Organization. Throughout the year, we observed an increasing trend in the prevalence of low-credibility news about vaccines. We also observed a considerable amount of suspicious YouTube videos shared on Twitter. Tweets by a small group of approximately 800 "superspreaders" verified by Twitter accounted for approximately 35% of all reshares of misinformation on an average day, with the top superspreader (@RobertKennedyJr) responsible for over 13% of retweets. Finally, low-credibility news and suspicious YouTube videos were more likely to be shared by automated accounts. CONCLUSIONS The wide spread of misinformation around COVID-19 vaccines on Twitter during 2021 shows that there was an audience for this type of content. Our findings are also consistent with the hypothesis that superspreaders are driven by financial incentives that allow them to profit from health misinformation. Despite high-profile cases of deplatformed misinformation superspreaders, our results show that in 2021, a few individuals still played an outsized role in the spread of low-credibility vaccine content. As a result, social media moderation efforts would be better served by focusing on reducing the online visibility of repeat spreaders of harmful content, especially during public health crises.
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Affiliation(s)
- Francesco Pierri
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
- Observatory on Social Media, Indiana University, Bloomington, IN, United States
| | - Matthew R DeVerna
- Observatory on Social Media, Indiana University, Bloomington, IN, United States
| | - Kai-Cheng Yang
- Observatory on Social Media, Indiana University, Bloomington, IN, United States
| | - David Axelrod
- Observatory on Social Media, Indiana University, Bloomington, IN, United States
| | - John Bryden
- Observatory on Social Media, Indiana University, Bloomington, IN, United States
| | - Filippo Menczer
- Observatory on Social Media, Indiana University, Bloomington, IN, United States
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Chen Y, Long J, Jun J, Kim SH, Zain A, Piacentine C. Anti-intellectualism amid the COVID-19 pandemic: The discursive elements and sources of anti-Fauci tweets. PUBLIC UNDERSTANDING OF SCIENCE (BRISTOL, ENGLAND) 2023:9636625221146269. [PMID: 36715354 PMCID: PMC9892881 DOI: 10.1177/09636625221146269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Anti-intellectualism (resentment, hostility, and mistrust of experts) has become a growing concern during the pandemic. Using topic modeling and supervised machine learning, this study examines the elements and sources of anti-Fauci tweets as a case of anti-intellectual discourse on social media. Based on the theoretical framework of science-related populism, we identified three anti-intellectual discursive elements in anti-Fauci tweets: people-scientist antagonism, delegitimizing the motivation of scientists, and delegitimizing the knowledge of scientists. Delegitimizing the motivation of scientists appeared the most in anti-Fauci tweets. Politicians, conservative news media, and non-institutional actors (e.g. individuals and grassroots advocacy organizations) co-constructed the production and circulation of anti-intellectual discourses on Twitter. Anti-intellectual discourses resurged even under Twitter's content moderation mechanism. We discuss theoretical and practical implications for building public trust in scientists, effective science communication, and content moderation policies on social media.
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Affiliation(s)
- Yingying Chen
- Yingying Chen, School of Journalism and
Communication, Renmin University of China, Mingde Building, 59 Zhongguancun
Street, Haidian, Beijing 100872, China.
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León-Quismondo J. Social Sensing and Individual Brands in Sports: Lessons Learned from English-Language Reactions on Twitter to Pau Gasol's Retirement Announcement. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:895. [PMID: 36673653 PMCID: PMC9859528 DOI: 10.3390/ijerph20020895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/23/2022] [Accepted: 12/31/2022] [Indexed: 06/17/2023]
Abstract
Pau Gasol announced his retirement on 5 October 2021. Subsequently, a number of users virtually reacted. Twitter is one of the most popular social media platforms, with more than 368 million active users, generating large-scale social data. This study used data from Twitter for analyzing social sensing related to an individual brand, Pau Gasol's retirement announcement, from a quantitative and qualitative content analysis perspective. Pau Gasol's farewell can be considered a unique event to which many people are emotionally attached, providing a great opportunity for understanding sports virtual ecosystems. A total of 2089 tweets in the English language were recovered from Tuesday 5 October 2021 at 3:00 to Thursday 7 October 2021 at 23:59, Greenwich Mean Time +00:00 time zone. During this time, posts were observed to be mainly influential during and right after Pau Gasol's ceremony. The tweets that created more impact were published by news sources or by sports reporters. Lastly, the themes that emerged showed that the Los Angeles Lakers and the NBA were the two most important milestones in Pau Gasol's career. The data can be used to detect potential areas of controversy or other issues to be addressed in order to preserve the athlete's public image. These results are considered of interest for reaching better knowledge of sport virtual environments through social sensing, supporting the idea of users acting as sensors.
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Aïmeur E, Amri S, Brassard G. Fake news, disinformation and misinformation in social media: a review. SOCIAL NETWORK ANALYSIS AND MINING 2023; 13:30. [PMID: 36789378 PMCID: PMC9910783 DOI: 10.1007/s13278-023-01028-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/07/2023] [Accepted: 01/12/2023] [Indexed: 02/12/2023]
Abstract
Online social networks (OSNs) are rapidly growing and have become a huge source of all kinds of global and local news for millions of users. However, OSNs are a double-edged sword. Although the great advantages they offer such as unlimited easy communication and instant news and information, they can also have many disadvantages and issues. One of their major challenging issues is the spread of fake news. Fake news identification is still a complex unresolved issue. Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial. On the other hand, artificial intelligence (AI) approaches are still incapable of overcoming this challenging problem. To make matters worse, AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content. Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. This work aims to provide a comprehensive and systematic review of fake news research as well as a fundamental review of existing approaches used to detect and prevent fake news from spreading via OSNs. We present the research problem and the existing challenges, discuss the state of the art in existing approaches for fake news detection, and point out the future research directions in tackling the challenges.
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Affiliation(s)
- Esma Aïmeur
- Department of Computer Science and Operations Research (DIRO), University of Montreal, Montreal, Canada
| | - Sabrine Amri
- Department of Computer Science and Operations Research (DIRO), University of Montreal, Montreal, Canada
| | - Gilles Brassard
- Department of Computer Science and Operations Research (DIRO), University of Montreal, Montreal, Canada
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31
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Jing J, Wu H, Sun J, Fang X, Zhang H. Multimodal fake news detection via progressive fusion networks. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2022.103120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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32
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Siebert J, Siebert JU. Effective mitigation of the belief perseverance bias after the retraction of misinformation: Awareness training and counter-speech. PLoS One 2023; 18:e0282202. [PMID: 36888583 PMCID: PMC9994702 DOI: 10.1371/journal.pone.0282202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 02/09/2023] [Indexed: 03/09/2023] Open
Abstract
The spread and influence of misinformation have become a matter of concern in society as misinformation can negatively impact individuals' beliefs, opinions and, consequently, decisions. Research has shown that individuals persevere in their biased beliefs and opinions even after the retraction of misinformation. This phenomenon is known as the belief perseverance bias. However, research on mitigating the belief perseverance bias after the retraction of misinformation has been limited. Only a few debiasing techniques with limited practical applicability have been proposed, and research on comparing various techniques in terms of their effectiveness has been scarce. This paper contributes to research on mitigating the belief perseverance bias after the retraction of misinformation by proposing counter-speech and awareness-training techniques and comparing them in terms of effectiveness to the existing counter-explanation technique in an experiment with N = 251 participants. To determine changes in opinions, the extent of the belief perseverance bias and the effectiveness of the debiasing techniques in mitigating the belief perseverance bias, we measure participants' opinions four times in the experiment by using Likert items and phi-coefficient measures. The effectiveness of the debiasing techniques is assessed by measuring the difference between the baseline opinions before exposure to misinformation and the opinions after exposure to a debiasing technique. Further, we discuss the efforts of the providers and recipients of debiasing and the practical applicability of the debiasing techniques. The CS technique, with a very large effect size, is the most effective among the three techniques. The CE and AT techniques, with medium effect sizes, are close to being equivalent in terms of their effectiveness. The CS and AT techniques are associated with less cognitive and time effort of the recipients of debiasing than the CE technique, while the AT and CE techniques require less effort from the providers of debiasing than the CS technique.
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Affiliation(s)
- Jana Siebert
- Department of Applied Economics, Faculty of Arts, Palacky University Olomouc, Olomouc, Czech Republic
| | - Johannes Ulrich Siebert
- Department of Business and Management, Management Center Innsbruck, Innsbruck, Austria
- * E-mail:
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33
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A systematic review of worldwide causal and correlational evidence on digital media and democracy. Nat Hum Behav 2023; 7:74-101. [PMID: 36344657 PMCID: PMC9883171 DOI: 10.1038/s41562-022-01460-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 09/16/2022] [Indexed: 11/09/2022]
Abstract
One of today's most controversial and consequential issues is whether the global uptake of digital media is causally related to a decline in democracy. We conducted a systematic review of causal and correlational evidence (N = 496 articles) on the link between digital media use and different political variables. Some associations, such as increasing political participation and information consumption, are likely to be beneficial for democracy and were often observed in autocracies and emerging democracies. Other associations, such as declining political trust, increasing populism and growing polarization, are likely to be detrimental to democracy and were more pronounced in established democracies. While the impact of digital media on political systems depends on the specific variable and system in question, several variables show clear directions of associations. The evidence calls for research efforts and vigilance by governments and civil societies to better understand, design and regulate the interplay of digital media and democracy.
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Pollack CC, Emond JA, O'Malley AJ, Byrd A, Green P, Miller KE, Vosoughi S, Gilbert-Diamond D, Onega T. Characterizing the Prevalence of Obesity Misinformation, Factual Content, Stigma, and Positivity on the Social Media Platform Reddit Between 2011 and 2019: Infodemiology Study. J Med Internet Res 2022; 24:e36729. [PMID: 36583929 PMCID: PMC9840103 DOI: 10.2196/36729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 06/06/2022] [Accepted: 09/07/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Reddit is a popular social media platform that has faced scrutiny for inflammatory language against those with obesity, yet there has been no comprehensive analysis of its obesity-related content. OBJECTIVE We aimed to quantify the presence of 4 types of obesity-related content on Reddit (misinformation, facts, stigma, and positivity) and identify psycholinguistic features that may be enriched within each one. METHODS All sentences (N=764,179) containing "obese" or "obesity" from top-level comments (n=689,447) made on non-age-restricted subreddits (ie, smaller communities within Reddit) between 2011 and 2019 that contained one of a series of keywords were evaluated. Four types of common natural language processing features were extracted: bigram term frequency-inverse document frequency, word embeddings derived from Bidirectional Encoder Representations from Transformers, sentiment from the Valence Aware Dictionary for Sentiment Reasoning, and psycholinguistic features from the Linguistic Inquiry and Word Count Program. These features were used to train an Extreme Gradient Boosting machine learning classifier to label each sentence as 1 of the 4 content categories or other. Two-part hurdle models for semicontinuous data (which use logistic regression to assess the odds of a 0 result and linear regression for continuous data) were used to evaluate whether select psycholinguistic features presented differently in misinformation (compared with facts) or stigma (compared with positivity). RESULTS After removing ambiguous sentences, 0.47% (3610/764,179) of the sentences were labeled as misinformation, 1.88% (14,366/764,179) were labeled as stigma, 1.94% (14,799/764,179) were labeled as positivity, and 8.93% (68,276/764,179) were labeled as facts. Each category had markers that distinguished it from other categories within the data as well as an external corpus. For example, misinformation had a higher average percent of negations (β=3.71, 95% CI 3.53-3.90; P<.001) but a lower average number of words >6 letters (β=-1.47, 95% CI -1.85 to -1.10; P<.001) relative to facts. Stigma had a higher proportion of swear words (β=1.83, 95% CI 1.62-2.04; P<.001) but a lower proportion of first-person singular pronouns (β=-5.30, 95% CI -5.44 to -5.16; P<.001) relative to positivity. CONCLUSIONS There are distinct psycholinguistic properties between types of obesity-related content on Reddit that can be leveraged to rapidly identify deleterious content with minimal human intervention and provide insights into how the Reddit population perceives patients with obesity. Future work should assess whether these properties are shared across languages and other social media platforms.
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Affiliation(s)
- Catherine C Pollack
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States
| | - Jennifer A Emond
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States
- Department of Pediatrics, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States
| | - A James O'Malley
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States
- The Dartmouth Institute for Health Policy and Clinical Practice, Hanover, NH, United States
| | - Anna Byrd
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States
| | - Peter Green
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States
| | - Katherine E Miller
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States
| | - Soroush Vosoughi
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
| | - Diane Gilbert-Diamond
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States
- Department of Pediatrics, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States
- Department of Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States
| | - Tracy Onega
- Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States
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35
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Jlifi B, Sakrani C, Duvallet C. Towards a soft three-level voting model (Soft T-LVM) for fake news detection. J Intell Inf Syst 2022; 61:1-21. [PMID: 36575748 PMCID: PMC9780098 DOI: 10.1007/s10844-022-00769-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/28/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022]
Abstract
Fake news has a worldwide impact and the potential to change political scenarios and human behavior, especially in a critical time like the COVID-19 pandemic. This work suggests a Soft Three-Level Voting Model (Soft T-LVM) for automatically classifying COVID-19 fake news. We train different individual machine learning algorithms and different ensemble methods in order to overcome the weakness of individual models. This novel model is based on the soft-voting technique to calculate the class with the majority of votes and to choose the classifiers to merge and apply at every level. We use the Grid search method to tune the hyper-parameters during the process of classification and voting. The experimental evaluation confirms that our proposed model approach has superior performance compared to the other classifiers.
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Affiliation(s)
- Boutheina Jlifi
- Ecole Supérieure de Commerce de Tunis (ESCT), LARIA Laboratory, University of Manouba, Manouba, Tunisia
| | - Chayma Sakrani
- Ecole Supérieure de Commerce de Tunis (ESCT), LARIA Laboratory, University of Manouba, Manouba, Tunisia
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36
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That's interesting! The role of epistemic emotions and perceived credibility in the relation between prior beliefs and susceptibility to fake-news. COMPUTERS IN HUMAN BEHAVIOR 2022. [DOI: 10.1016/j.chb.2022.107619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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37
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Sakthivel RK, Nagasubramanian G, Sankayya M, Al-Turjman F. Multilingual News Feed Analysis Using Intelligent Linguistic Particle Filtering Techniques. ACM T ASIAN LOW-RESO 2022. [DOI: 10.1145/3569899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Analyzing real-time news feeds and their impacts in the real world is a complex task in the social networking arena. Particularly, countries with a multilingual environment have various patterns and perceptions of news reports considering the diversity of the people. Multilingual and multimodal news analysis is an emerging trend for evaluating news source neutralities. Therefore, in this work, four new deep news particle filtering techniques were developed, including generic news analysis, sequential importance re-sampling (SIR)-based news particle filtering analysis, reinforcement learning (RL)-based multimodal news analysis, and deep Convolution neural network (DCNN)-based multi-news filtering approach, for news classification. Results indicate that these techniques, which primarily employ particle filtering with multilevel sampling strategies, produce 15% to 20% better performance than conventional news analysis techniques.
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Affiliation(s)
| | | | | | - Fadi Al-Turjman
- Artificial Intelligence Engineering Dept., AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey
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38
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Ali AM, Ghaleb FA, Al-Rimy BAS, Alsolami FJ, Khan AI. Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique. SENSORS (BASEL, SWITZERLAND) 2022; 22:6970. [PMID: 36146319 PMCID: PMC9504299 DOI: 10.3390/s22186970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/02/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community's behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency-inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques.
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Affiliation(s)
- Abdullah Marish Ali
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Fuad A. Ghaleb
- Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
- Department of Computer Engineering and Electronics, Sanaá Community College, Sanaá 5695, Yemen
| | - Bander Ali Saleh Al-Rimy
- Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
| | - Fawaz Jaber Alsolami
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Asif Irshad Khan
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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39
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Tian Y, Lambiotte R. Unifying information propagation models on networks and influence maximization. Phys Rev E 2022; 106:034316. [PMID: 36266854 DOI: 10.1103/physreve.106.034316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/30/2022] [Indexed: 06/16/2023]
Abstract
Information propagation on networks is a central theme in social, behavioral, and economic sciences, with important theoretical and practical implications, such as the influence maximization problem for viral marketing. Here we consider a model that unifies the classical independent cascade models and the linear threshold models, and generalize them by considering continuous variables and allowing feedback in the dynamics. We then formulate its influence maximization as a mixed integer nonlinear programming problem and adopt derivative-free methods. Furthermore, we show that the problem can be exactly solved in the special case of linear dynamics, where the selection criterion is closely related to the Katz centrality, and propose a customized direct search method with local convergence. We then demonstrate the close to optimal performance of the customized direct search numerically on both synthetic and real networks.
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Affiliation(s)
- Yu Tian
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom
| | - Renaud Lambiotte
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom
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Social diffusion sources can escape detection. iScience 2022; 25:104956. [PMID: 36093057 PMCID: PMC9459693 DOI: 10.1016/j.isci.2022.104956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/25/2022] [Accepted: 08/12/2022] [Indexed: 11/16/2022] Open
Abstract
Influencing others through social networks is fundamental to all human societies. Whether this happens through the diffusion of rumors, opinions, or viruses, identifying the diffusion source (i.e., the person that initiated it) is a problem that has attracted much research interest. Nevertheless, existing literature has ignored the possibility that the source might strategically modify the network structure (by rewiring links or introducing fake nodes) to escape detection. Here, without restricting our analysis to any particular diffusion scenario, we close this gap by evaluating two mechanisms that hide the source—one stemming from the source’s actions, the other from the network structure itself. This reveals that sources can easily escape detection, and that removing links is far more effective than introducing fake nodes. Thus, efforts should focus on exposing concealed ties rather than planted entities; such exposure would drastically improve our chances of detecting the diffusion source. We study the problem of hiding the diffusion source from source detection algorithms Finding an optimal way of hiding the source is usually computationally intractable In many cases, the source is well hidden without any strategic modifications If the source is exposed, simple heuristic solutions allow it to avoid detection
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Yang KC, Ferrara E, Menczer F. Botometer 101: social bot practicum for computational social scientists. JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE 2022; 5:1511-1528. [PMID: 36035522 PMCID: PMC9391657 DOI: 10.1007/s42001-022-00177-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 08/01/2022] [Indexed: 05/16/2023]
Abstract
Social bots have become an important component of online social media. Deceptive bots, in particular, can manipulate online discussions of important issues ranging from elections to public health, threatening the constructive exchange of information. Their ubiquity makes them an interesting research subject and requires researchers to properly handle them when conducting studies using social media data. Therefore, it is important for researchers to gain access to bot detection tools that are reliable and easy to use. This paper aims to provide an introductory tutorial of Botometer, a public tool for bot detection on Twitter, for readers who are new to this topic and may not be familiar with programming and machine learning. We introduce how Botometer works, the different ways users can access it, and present a case study as a demonstration. Readers can use the case study code as a template for their own research. We also discuss recommended practice for using Botometer.
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Affiliation(s)
- Kai-Cheng Yang
- Observatory on Social Media, Indiana University Bloomington, Bloomington, IN 47408 USA
| | - Emilio Ferrara
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292 USA
| | - Filippo Menczer
- Observatory on Social Media, Indiana University Bloomington, Bloomington, IN 47408 USA
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Gao Y, Su X, Wei J, Sun J, Zhang M, Tan H, Zhang J, Ouyang J, Na N. Water-resistant organic-inorganic hybrid perovskite quantum dots activated by the electron-deficient d-orbital of platinum atoms for nitrogen fixation. NANOSCALE 2022; 14:10780-10792. [PMID: 35861174 DOI: 10.1039/d2nr02662g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Due to their special physicochemical properties, organic-inorganic hybrid perovskite quantum dots (OIP QDs) are ideal and potential catalysts for the nitrogen reduction reaction (NRR). However, the OIP QD-based NRR is limited by poor water resistance, competitive suppression by the hydrogen evolution reaction, and inefficient active sites on the catalyst surfaces. Herein, to ensure an efficient NRR in aqueous solution, a water-resistant polycarbonate-part-encapsulated heterojunction of Zn,PtIV co-doped PbO-MAPbBr3 (PtIV/Zn/PbO/PC-Zn/MAPbBr3) is prepared through one-step electrospray-based microdroplet synthesis. Confirmed by both experimental and theoretical examinations, PbO is exposed on the PC-part-encapsulated surface to construct a Type I heterojunction. This heterojunction is further improved by synergistic co-doping with PtIV to facilitate efficient electron transfer for efficient photocatalysis of the NRR. Due to the active sites of the d-orbital electron-deficient Pt atoms (exhibiting a lower reaction energy barrier and highly selective N2 adsorption), the ammonia yield rate is 40 times higher than that without doping. This work initiates and develops on the application of OIP QDs in the NRR.
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Affiliation(s)
- Yixuan Gao
- Key Laboratory of Radiopharmaceuticals, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
| | - Xiao Su
- Key Laboratory of Radiopharmaceuticals, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
| | - Juanjuan Wei
- Key Laboratory of Radiopharmaceuticals, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
| | - Jianghui Sun
- Key Laboratory of Radiopharmaceuticals, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
| | - Min Zhang
- Key Laboratory of Radiopharmaceuticals, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
| | - Hongwei Tan
- Key Laboratory of Radiopharmaceuticals, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
| | - Jiangwei Zhang
- Dalian National Laboratory for Clean Energy & State, Key Laboratory of Catalysis, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), China.
| | - Jin Ouyang
- Key Laboratory of Radiopharmaceuticals, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
| | - Na Na
- Key Laboratory of Radiopharmaceuticals, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
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43
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Iida T, Song J, Estrada JL, Takahashi Y. Fake news and its electoral consequences: a survey experiment on Mexico. AI & SOCIETY 2022. [DOI: 10.1007/s00146-022-01541-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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44
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Identify influential nodes in network of networks from the view of weighted information fusion. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03856-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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45
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46
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Yang KC, Hui PM, Menczer F. How Twitter data sampling biases U.S. voter behavior characterizations. PeerJ Comput Sci 2022; 8:e1025. [PMID: 35875635 PMCID: PMC9299280 DOI: 10.7717/peerj-cs.1025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
Online social media are key platforms for the public to discuss political issues. As a result, researchers have used data from these platforms to analyze public opinions and forecast election results. The literature has shown that due to inauthentic actors such as malicious social bots and trolls, not every message is a genuine expression from a legitimate user. However, the prevalence of inauthentic activities in social data streams is still unclear, making it difficult to gauge biases of analyses based on such data. In this article, we aim to close this gap using Twitter data from the 2018 U.S. midterm elections. We propose an efficient and low-cost method to identify voters on Twitter and systematically compare their behaviors with different random samples of accounts. We find that some accounts flood the public data stream with political content, drowning the voice of the majority of voters. As a result, these hyperactive accounts are over-represented in volume samples. Hyperactive accounts are more likely to exhibit various suspicious behaviors and to share low-credibility information compared to likely voters. Our work provides insights into biased voter characterizations when using social media data to analyze political issues.
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47
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Chang HCH, Ferrara E. Comparative analysis of social bots and humans during the COVID-19 pandemic. JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE 2022; 5:1409-1425. [PMID: 35789937 PMCID: PMC9244092 DOI: 10.1007/s42001-022-00173-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 05/14/2022] [Indexed: 06/15/2023]
Abstract
Using more than 4 billion tweets and labels on more than 5 million users, this paper compares the behavior of humans and bots politically and semantically during the pandemic. Results reveal liberal bots are more central than humans in general, but less important than institutional humans as the elite circle grows smaller. Conservative bots are surprisingly absent when compared to prior work on political discourse, but are better than liberal bots at eliciting replies from humans, which suggest they may be perceived as human more frequently. In terms of topic and framing, conservative humans and bots disproportionately tweet about the Bill Gates and bio-weapons conspiracy, whereas the 5G conspiracy is bipartisan. Conservative humans selectively ignore mask-wearing and we observe prevalent out-group tweeting when discussing policy. We discuss and contrast how humans appear more centralized in health-related discourse as compared to political events, which suggests the importance of credibility and authenticity for public health in online information diffusion.
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Affiliation(s)
- Ho-Chun Herbert Chang
- Annenberg School for Communication and Journalism, University of Southern California, Los Angeles, CA 90007 USA
- Information Science Institute, Los Angeles, CA 90292 USA
| | - Emilio Ferrara
- Information Science Institute, Los Angeles, CA 90292 USA
- Viterbi School of Engineering, USC, Los Angeles, CA 90007 USA
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48
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Cinelli M, Etta G, Avalle M, Quattrociocchi A, Di Marco N, Valensise C, Galeazzi A, Quattrociocchi W. Conspiracy theories and social media platforms. Curr Opin Psychol 2022; 47:101407. [PMID: 35868169 DOI: 10.1016/j.copsyc.2022.101407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/17/2022] [Accepted: 06/20/2022] [Indexed: 11/03/2022]
Abstract
Conspiracy theories proliferate online. We provide an overview of information consumption patterns related to conspiracy content on four mainstream social media platforms (Facebook, Twitter, YouTube, and Reddit), with a focus on niche ones. Opinion polarisation and echo chambers appear as pivotal elements of communication around conspiracy theories. A relevant role may also be played by the content moderation policies enforced by each social media platform. Banning contents or users from a social media could lead to a level of user segregation that goes beyond echo chambers and reaches the entire social media space, up to the formation of 'echo platforms'. The insurgence of echo platforms is a new online phenomenon that needs to be investigated as it could foster many dangerous phenomena that we observe online, including the spreading of conspiracy theories.
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Affiliation(s)
- Matteo Cinelli
- Sapienza University of Rome - Department of Computer Science Viale Regina Elena, 295, 00100 Rome, Italy
| | - Gabriele Etta
- Sapienza University of Rome - Department of Computer Science Viale Regina Elena, 295, 00100 Rome, Italy
| | - Michele Avalle
- Sapienza University of Rome - Department of Computer Science Viale Regina Elena, 295, 00100 Rome, Italy
| | - Alessandro Quattrociocchi
- Sapienza University of Rome - Department of Computer Science Viale Regina Elena, 295, 00100 Rome, Italy
| | - Niccolò Di Marco
- University of Florence, - Department of Mathematics and Computer Science, Viale Giovanni Battista Morgagni, 67/a, 50134 Florence, Italy
| | - Carlo Valensise
- Enrico Fermi Research Center, Piazza del Viminale 1, Rome 00184, Italy
| | - Alessandro Galeazzi
- Ca' Foscari, University of Venice - Department of Environmental Sciences, Informatics and Statistics, Via Torino 155, 30172 Mestre Italy
| | - Walter Quattrociocchi
- Sapienza University of Rome - Department of Computer Science Viale Regina Elena, 295, 00100 Rome, Italy.
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49
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Zhang R, Sun S, Zhang F, Chen K, Liu L, Zhu N. Four-mode parallel silicon multimode waveguide crossing scheme based on the asymmetric directional couplers. OPTICS EXPRESS 2022; 30:22442-22451. [PMID: 36224942 DOI: 10.1364/oe.459968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/25/2022] [Indexed: 06/16/2023]
Abstract
We theoretically propose and experimentally demonstrate a novel ultra-compact four-mode silicon waveguide crossing device based on the asymmetric directional couplers for densely integrated on-chip mode division multiplexing systems. The crossing is based on the parallel crossing scheme where the two access waveguides are parallel to each other to have minimal area. The device utilizes an idle high order mode inside one bus waveguide to drop subsequently all the guided modes inside another bus waveguide, with the help of the asymmetric directional couplers (ADCs). We also optimize the structural parameters of these ADCs by using the particle swarm optimization method to obtain higher conversion efficiency and smaller coupling length. The simulation results show that the insertion losses of the input 1-8 ports are no more than 0.5 dB at the central wavelength of 1550 nm. And the crosstalks are less than -20 dB in the broadband from 1530 nm to 1580 nm with a footprint of only 25 × 70 µm2. Furthermore, our scheme can be easily extended to accommodate more modes by cascading more ADCs for mode dropping and crossing, without obviously deteriorating the performance and greatly increasing the overall footprint.
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50
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Ali RH, Pinto G, Lawrie E, Linstead EJ. A large-scale sentiment analysis of tweets pertaining to the 2020 US presidential election. JOURNAL OF BIG DATA 2022; 9:79. [PMID: 35729897 PMCID: PMC9202966 DOI: 10.1186/s40537-022-00633-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 06/05/2022] [Indexed: 06/15/2023]
Abstract
We capture the public sentiment towards candidates in the 2020 US Presidential Elections, by analyzing 7.6 million tweets sent out between October 31st and November 9th, 2020. We apply a novel approach to first identify tweets and user accounts in our database that were later deleted or suspended from Twitter. This approach allows us to observe the sentiment held for each presidential candidate across various groups of users and tweets: accessible tweets and accounts, deleted tweets and accounts, and suspended or inaccessible tweets and accounts. We compare the sentiment scores calculated for these groups and provide key insights into the differences. Most notably, we show that deleted tweets, posted after the Election Day, were more favorable to Joe Biden, and the ones posted leading to the Election Day, were more positive about Donald Trump. Also, the older a Twitter account was, the more positive tweets it would post about Joe Biden. The aim of this study is to highlight the importance of conducting sentiment analysis on all posts captured in real time, including those that are now inaccessible, in determining the true sentiments of the opinions around the time of an event.
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Affiliation(s)
- Rao Hamza Ali
- Fowler School of Engineering, Chapman University, One University Drive, Orange, California USA
| | - Gabriela Pinto
- Fowler School of Engineering, Chapman University, One University Drive, Orange, California USA
| | - Evelyn Lawrie
- Fowler School of Engineering, Chapman University, One University Drive, Orange, California USA
| | - Erik J. Linstead
- Fowler School of Engineering, Chapman University, One University Drive, Orange, California USA
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