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Deng W, Yang Y. Cross-Platform Comparative Study of Public Concern on Social Media during the COVID-19 Pandemic: An Empirical Study Based on Twitter and Weibo. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6487. [PMID: 34208483 PMCID: PMC8296381 DOI: 10.3390/ijerph18126487] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/31/2021] [Accepted: 06/12/2021] [Indexed: 11/24/2022]
Abstract
The COVID-19 pandemic has created a global health crisis that has affected economies and societies worldwide. During these times of uncertainty and crisis, people have turned to social media platforms as communication tools and primary information sources. Online discourse is conducted under the influence of many different factors, such as background, culture, politics, etc. However, parallel comparative research studies conducted in different countries to identify similarities and differences in online discourse are still scarce. In this study, we combine the crisis lifecycle and opinion leader concepts and use data mining and a set of predefined search terms (coronavirus and COVID-19) to investigate discourse on Twitter (101,271 tweets) and Sina Weibo (92,037 posts). Then, we use a topic modeling technique, Latent Dirichlet Allocation (LDA), to identify the most common issues posted by users and temporal analysis to research the issue's trend. Social Network Analysis (SNA) allows us to discover the opinion leader on the two different platforms. Finally, we find that online discourse reflects the crisis lifecycle according to the stage of COVID-19 in China and the US. Regarding the status of the COVID-19 pandemic, users of Twitter tend to pay more attention to the economic situation while users of Weibo pay more attention to public health. The issues focused on in online discourse have a strong relationship with the development of the crisis in different countries. Additionally, on the Twitter platform many political actors act as opinion leaders, while on the Weibo platform official media and government accounts control the release of information.
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Affiliation(s)
| | - Yi Yang
- College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China;
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Olivares R, Muñoz F, Riquelme F. A multi-objective linear threshold influence spread model solved by swarm intelligence-based methods. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106623] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Pensa RG, Di Blasi G, Bioglio L. Network-aware privacy risk estimation in online social networks. SOCIAL NETWORK ANALYSIS AND MINING 2019. [DOI: 10.1007/s13278-019-0558-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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5
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Riquelme F, Gonzalez-Cantergiani P, Molinero X, Serna M. Centrality measure in social networks based on linear threshold model. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2017.10.029] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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6
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7
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Information Is Not a Virus, and Other Consequences of Human Cognitive Limits. FUTURE INTERNET 2016. [DOI: 10.3390/fi8020021] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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Saito K, Kimura M, Ohara K, Motoda H. Super mediator – A new centrality measure of node importance for information diffusion over social network. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2015.03.034] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Zhang X, Chen X, Chen Y, Wang S, Li Z, Xia J. Event detection and popularity prediction in microblogging. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.08.045] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Zhang Y, Li X. Relative Superiority of Key Centrality Measures for Identifying Influencers on Social Media. INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2014. [DOI: 10.4018/ijiit.2014100101] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Marketers have been increasingly turning to social media for marketing campaigns, including viral marketing. A key step in viral marketing is to identify influencers in order to maximize the reach of a marketing message. Existing research shows that centrality measures, such as degree and betweenness, are effective methods for influencer identification. However, viral marketing models used in different studies vary greatly, making it difficult to compare findings across the studies. In this paper, the authors built an agent-based framework of viral marketing that supports different experiment settings, such as different network structures and information diffusion modes, and used it to study relative superiority of various centrality measures. The results show that relative superiority of the measures are affected by some factors, but not as much by others. Practical implications of the results are discussed.
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Affiliation(s)
- Yifeng Zhang
- Department of Management Information Systems, University of Illinois at Springfield, Springfield, IL, USA
| | - Xiaoqing Li
- Department of Management Information Systems, University of Illinois at Springfield, Springfield, IL, USA
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Cataldi M, Aufaure MA. The 10 million follower fallacy: audience size does not prove domain-influence on Twitter. Knowl Inf Syst 2014. [DOI: 10.1007/s10115-014-0773-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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13
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Hodas NO, Lerman K. The simple rules of social contagion. Sci Rep 2014; 4:4343. [PMID: 24614301 PMCID: PMC3949249 DOI: 10.1038/srep04343] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2013] [Accepted: 02/24/2014] [Indexed: 11/16/2022] Open
Abstract
It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention, and explicit social feedback to predict the temporal dynamics of user behavior.
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Affiliation(s)
- Nathan O Hodas
- USC Information Sciences Institute, Marina del Rey, CA 90292
| | - Kristina Lerman
- USC Information Sciences Institute, Marina del Rey, CA 90292
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Saito K, Kimura M, Ohara K, Motoda H. Detecting changes in information diffusion patterns over social networks. ACM T INTEL SYST TEC 2013. [DOI: 10.1145/2483669.2483688] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
We addressed the problem of detecting the change in behavior of information diffusion over a social network which is caused by an unknown external situation change using a small amount of observation data in a retrospective setting. The unknown change is assumed effectively reflected in changes in the parameter values in the probabilistic information diffusion model, and the problem is reduced to detecting where in time and how long this change persisted and how big this change is. We solved this problem by searching the change pattern that maximizes the likelihood of generating the observed information diffusion sequences, and in doing so we devised a very efficient general iterative search algorithm using the derivative of the likelihood which avoids parameter value optimization during each search step. This is in contrast to the naive learning algorithm in that it has to iteratively update the patten boundaries, each requiring the parameter value optimization and thus is very inefficient. We tested this algorithm for two instances of the probabilistic information diffusion model which has different characteristics. One is of information push style and the other is of information pull style. We chose Asynchronous Independent Cascade (AsIC) model as the former and Value-weighted Voter (VwV) model as the latter. The AsIC is the model for general information diffusion with binary states and the parameter to detect its change is diffusion probability and the VwV is the model for opinion formation with multiple states and the parameter to detect its change is opinion value. The results tested on these two models using four real-world network structures confirm that the algorithm is robust enough and can efficiently identify the correct change pattern of the parameter values. Comparison with the naive method that finds the best combination of change boundaries by an exhaustive search through a set of randomly selected boundary candidates shows that the proposed algorithm far outperforms the native method both in terms of accuracy and computation time.
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Saito K, Kimura M, Ohara K, Motoda H. Which Targets to Contact First to Maximize Influence over Social Network. SOCIAL COMPUTING, BEHAVIORAL-CULTURAL MODELING AND PREDICTION 2013. [DOI: 10.1007/978-3-642-37210-0_39] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
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16
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Learning to predict opinion share and detect anti-majority opinionists in social networks. J Intell Inf Syst 2012. [DOI: 10.1007/s10844-012-0222-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Saito K, Kimura M, Ohara K, Motoda H. Behavioral Analyses of Information Diffusion Models by Observed Data of Social Network. ADVANCES IN SOCIAL COMPUTING 2010. [DOI: 10.1007/978-3-642-12079-4_20] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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20
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Characteristics of the Dynamic of Mobile Networks. LECTURE NOTES OF THE INSTITUTE FOR COMPUTER SCIENCES, SOCIAL INFORMATICS AND TELECOMMUNICATIONS ENGINEERING 2010. [PMCID: PMC7120974 DOI: 10.1007/978-3-642-12808-0_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
We propose in this paper a novel framework for the study of dynamic mobility networks. We address the characterization of dynamics by proposing an in-depth description and analysis of two real-world data sets. We show in particular that links creation and deletion processes are independent of other graph properties and that such networks exhibit a large number of possible configurations, from sparse to dense. From those observations, we propose simple yet very accurate models that allow to generate random mobility graphs with similar temporal behavior as the one observed in experimental data.
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Selecting Information Diffusion Models over Social Networks for Behavioral Analysis. MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES 2010. [DOI: 10.1007/978-3-642-15939-8_12] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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22
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Lin D, Li S, Cao D. Making intelligent business decisions by mining the implicit relation from bloggers’ posts. Soft comput 2009. [DOI: 10.1007/s00500-009-0499-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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23
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Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis. LECTURE NOTES IN COMPUTER SCIENCE 2009. [DOI: 10.1007/978-3-642-05224-8_25] [Citation(s) in RCA: 74] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Abstract
Weblogs, or Blogs, have facilitated people to express their thoughts, voice their opinions, and share their experiences and ideas. Individuals experience a sense of community, a feeling of belonging, a bonding that members matter to one another and their niche needs will be met through online interactions. Its open standards and low barrier to publication have transformed information consumers to producers. This has created a plethora of open-source intelligence, or "collective wisdom" that acts as the storehouse of over-whelming amounts of knowledge about the members, their environment and the symbiosis between them. Nonetheless, vast amounts of this knowledge still remain to be discovered and exploited in its suitable way. In this paper, we introduce various state-of-the-art research issues, review some key elements of research such as tools and methodologies in Blogosphere, and present a case study of identifying the influential bloggers in a community to exemplify the integration of some major aspects discussed in this paper. Towards the end, we also compare and contrast the blogosphere and social networks and the research therein.
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Affiliation(s)
| | - Huan Liu
- Arizona State University, Tempe, AZ
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25
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Patterns of Influence in a Recommendation Network. ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING 2006. [DOI: 10.1007/11731139_44] [Citation(s) in RCA: 150] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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