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St-Onge G, Hébert-Dufresne L, Allard A. Nonlinear bias toward complex contagion in uncertain transmission settings. Proc Natl Acad Sci U S A 2024; 121:e2312202121. [PMID: 38154065 PMCID: PMC10769855 DOI: 10.1073/pnas.2312202121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 11/24/2023] [Indexed: 12/30/2023] Open
Abstract
Current epidemics in the biological and social domains are challenging the standard assumptions of mathematical contagion models. Chief among them are the complex patterns of transmission caused by heterogeneous group sizes and infection risk varying by orders of magnitude in different settings, like indoor versus outdoor gatherings in the COVID-19 pandemic or different moderation practices in social media communities. However, quantifying these heterogeneous levels of risk is difficult, and most models typically ignore them. Here, we include these features in an epidemic model on weighted hypergraphs to capture group-specific transmission rates. We study analytically the consequences of ignoring the heterogeneous transmissibility and find an induced superlinear infection rate during the emergence of a new outbreak, even though the underlying mechanism is a simple, linear contagion. The dynamics produced at the individual and group levels are therefore more similar to complex, nonlinear contagions, thus blurring the line between simple and complex contagions in realistic settings. We support this claim by introducing a Bayesian inference framework to quantify the nonlinearity of contagion processes. We show that simple contagions on real weighted hypergraphs are systematically biased toward the superlinear regime if the heterogeneity of the weights is ignored, greatly increasing the risk of erroneous classification as complex contagions. Our results provide an important cautionary tale for the challenging task of inferring transmission mechanisms from incidence data. Yet, it also paves the way for effective models that capture complex features of epidemics through nonlinear infection rates.
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Affiliation(s)
- Guillaume St-Onge
- Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA02115
| | - Laurent Hébert-Dufresne
- Vermont Complex Systems Center, University of Vermont, Burlington, VT05401
- Department of Computer Science, University of Vermont, Burlington, VT05401
- Département de physique, de génie physique et d’optique, Université Laval, Québec, QCG1V 0A6, Canada
| | - Antoine Allard
- Vermont Complex Systems Center, University of Vermont, Burlington, VT05401
- Département de physique, de génie physique et d’optique, Université Laval, Québec, QCG1V 0A6, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, QCG1V 0A6, Canada
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2
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Suzuki D, Tsugawa S, Tsukamoto K, Igari S. On the effectiveness of a contrastive cascade graph learning framework: The power of synthetic cascade data. PLoS One 2023; 18:e0293032. [PMID: 37844089 PMCID: PMC10578604 DOI: 10.1371/journal.pone.0293032] [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: 03/26/2023] [Accepted: 10/03/2023] [Indexed: 10/18/2023] Open
Abstract
Analyzing the dynamics of information diffusion cascades and accurately predicting their behavior holds significant importance in various applications. In this paper, we concentrate specifically on a recently introduced contrastive cascade graph learning framework, for the task of predicting cascade popularity. This framework follows a pre-training and fine-tuning paradigm to address cascade prediction tasks. In a previous study, the transferability of pre-trained models within the contrastive cascade graph learning framework was examined solely between two social media datasets. However, in our present study, we comprehensively evaluate the transferability of pre-trained models across 13 real datasets and six synthetic datasets. We construct several pre-trained models using real cascades and synthetic cascades generated by the independent cascade model and the Profile model. Then, we fine-tune these pre-trained models on real cascade datasets and evaluate their prediction accuracy based on the mean squared logarithmic error. The main findings derived from our results are as follows. (1) The pre-trained models exhibit transferability across diverse types of real datasets in different domains, encompassing different languages, social media platforms, and diffusion time scales. (2) Synthetic cascade data prove effective for pre-training purposes. The pre-trained models constructed with synthetic cascade data demonstrate comparable effectiveness to those constructed using real data. (3) Synthetic cascade data prove beneficial for fine-tuning the contrastive cascade graph learning models and training other state-of-the-art popularity prediction models. Models trained using a combination of real and synthetic cascades yield significantly lower mean squared logarithmic error compared to those trained solely on real cascades. Our findings affirm the effectiveness of synthetic cascade data in enhancing the accuracy of cascade popularity prediction.
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Affiliation(s)
- Daiki Suzuki
- Graduate School of Engineering Information and Systems, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Sho Tsugawa
- Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan
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3
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Berkowitz HE, Vann JCJ. Strategies to Address COVID-19 Vaccine and Pregnancy Myths. MCN Am J Matern Child Nurs 2023; 48:215-223. [PMID: 36943837 PMCID: PMC10296984 DOI: 10.1097/nmc.0000000000000926] [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] [Indexed: 03/23/2023]
Abstract
ABSTRACT Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses risks to pregnant women and their infants. The spread of misinformation about COVID-19 vaccination is a barrier to optimizing vaccination rates among women of childbearing age. We conducted an environmental scan to identify misinformation about COVID-19 vaccination, pregnancy, and fertility, and a review to identify evidence to refute misinformation and strategies to correct and prevent the spread of misinformation. Seven identified themes of misinformation are: the vaccine causes female infertility; can cause miscarriage; and can decrease male fertility; mRNA vaccines attack the placenta; pregnant and breastfeeding persons should not get the vaccine; the vaccine can change menstrual cycles; and vaccinated people can spread infertility symptoms to unvaccinated people. Strategies that can be implemented by social media platforms to help prevent misinformation spread and correct existing health misinformation include improving information regulation by modifying community standards, implementing surveillance algorithms, and applying warning labels to potentially misleading posts. Health services organizations and clinicians can implement health misinformation policies, directly recommend vaccinations, provide credible explanations and resources to debunk misinformation, educate patients and populations on spotting misinformation, and apply effective communication strategies. More research is needed to assess longer-term effects of vaccination among women of childbearing age to strengthen the defense against misinformation and to evaluate strategies that aim to prevent and correct misinformation spread about COVID-19 vaccinations.
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Beck KB, Sheldon BC, Firth JA. Social learning mechanisms shape transmission pathways through replicate local social networks of wild birds. eLife 2023; 12:85703. [PMID: 37128701 PMCID: PMC10154030 DOI: 10.7554/elife.85703] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/05/2023] [Indexed: 05/03/2023] Open
Abstract
The emergence and spread of novel behaviours via social learning can lead to rapid population-level changes whereby the social connections between individuals shape information flow. However, behaviours can spread via different mechanisms and little is known about how information flow depends on the underlying learning rule individuals employ. Here, comparing four different learning mechanisms, we simulated behavioural spread on replicate empirical social networks of wild great tits and explored the relationship between individual sociality and the order of behavioural acquisition. Our results reveal that, for learning rules dependent on the sum and strength of social connections to informed individuals, social connectivity was related to the order of acquisition, with individuals with increased social connectivity and reduced social clustering adopting new behaviours faster. However, when behavioural adoption depends on the ratio of an individuals' social connections to informed versus uninformed individuals, social connectivity was not related to the order of acquisition. Finally, we show how specific learning mechanisms may limit behavioural spread within networks. These findings have important implications for understanding whether and how behaviours are likely to spread across social systems, the relationship between individuals' sociality and behavioural acquisition, and therefore for the costs and benefits of sociality.
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Affiliation(s)
- Kristina B Beck
- Edward Grey Institute of Field Ornithology, Department of Biology, University of Oxford, Oxford, United Kingdom
| | - Ben C Sheldon
- Edward Grey Institute of Field Ornithology, Department of Biology, University of Oxford, Oxford, United Kingdom
| | - Josh A Firth
- Edward Grey Institute of Field Ornithology, Department of Biology, University of Oxford, Oxford, United Kingdom
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5
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Yang C, Wang H, Tang J, Shi C, Sun M, Cui G, Liu Z. Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:2271-2283. [PMID: 34469314 DOI: 10.1109/tnnls.2021.3106156] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Information diffusion prediction is an important task, which studies how information items spread among users. With the success of deep learning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction, which aims at guessing who will be the next influenced user at what time, or macroscopic diffusion prediction, which estimates the total numbers of influenced users during the diffusion process. To the best of our knowledge, few attempts have been made to suggest a unified model for both microscopic and macroscopic scales. In this article, we propose a novel full-scale diffusion prediction model based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information into the RNN-based microscopic diffusion model by addressing the nondifferentiable problem. We also employ an effective structural context extraction strategy to utilize the underlying social graph information. Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets.
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6
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Martínez V, Jiménez-Molina Á, Gerber MM. Social contagion, violence, and suicide among adolescents. Curr Opin Psychiatry 2023; 36:237-242. [PMID: 36762666 PMCID: PMC10090320 DOI: 10.1097/yco.0000000000000858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
PURPOSE OF REVIEW Social Contagion is defined as the spread of behaviors, attitudes, and affect through crowds and other types of social aggregates from one member to another. Adolescents are prone to social contagion because they may be especially susceptible to peer influence and social media.In this article, we provide a brief review of the most recent findings on social contagion, violence, and suicide among adolescents. RECENT FINDINGS Recent evidence support social contagion in gun violence, bullying, cyberbullying, violent offending, and suicide, but is inconclusive on the role of violent video game exposure on aggressive behavior. SUMMARY The mechanisms underlying the contagion effect of violence and suicide are currently unclear. It has been argued that social learning, identification with significant others, and the normalization of specific norms play a role. All these mechanisms require understanding social contagion as a complex interaction between individual, relational and social factors. This is key if the social contagion perspective is to be used not only to investigate negative outcomes, but also as a framework for promoting prosocial attitudes and behaviors. Additionally, more research is needed on psychosocial interventions and public policies to minimize the potential spillover effect of violence and suicide.
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Affiliation(s)
- Vania Martínez
- CEMERA, Facultad de Medicina, Universidad de Chile, Santiago
- Millennium Nucleus to Improve the Mental Health of Adolescents and Youths (Imhay)
- Millennium Institute for Research in Depression and Personality (MIDAP)
| | - Álvaro Jiménez-Molina
- Millennium Nucleus to Improve the Mental Health of Adolescents and Youths (Imhay)
- Millennium Institute for Research in Depression and Personality (MIDAP)
- Facultad de Psicología, Universidad Diego Portales, Santiago, Chile
| | - Mónica M. Gerber
- Facultad de Psicología, Universidad Diego Portales, Santiago, Chile
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7
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Koval A, Beasley WH, Hararuk O, Rodgers JL. Social Contagion and General Diffusion Models of Adolescent Religious Transitions: A Tutorial, and EMOSA Applications. JOURNAL OF RESEARCH ON ADOLESCENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR RESEARCH ON ADOLESCENCE 2023; 33:318-343. [PMID: 34889482 DOI: 10.1111/jora.12695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Epidemic Models of the Onset of Social Activities (EMOSA) describe behaviors that spread through social networks. Two social influence methods are represented, social contagion (one-to-one spread) and general diffusion (spread through cultural channels). Past models explain problem behaviors-smoking, drinking, sexuality, and delinquency. We provide review, and a tutorial (including examples). Following, we present new EMOSA models explaining changes in adolescent and young adult religious participation. We fit the model to 10 years of data from the 1997 U.S. National Longitudinal Survey of Youth. Innovations include a three-stage bi-directional model, Bayesian Markov Chain Monte Carlo (MCMC) estimation, graphical innovations, and empirical validation. General diffusion dominated rapid reduction in church attendance during adolescence; both diffusion and social contagion explained church attendance stability in early adulthood.
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8
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Hierarchical Attention Neural Network for Information Cascade Prediction. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.11.163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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9
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System dynamics life cycle-based carbon model for consumption changes in urban metabolism. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2022.110010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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10
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Pham MA, Scott SB, Fyie LR, Gardiner MM. Sustainable landscaping programs in the United States and their potential to encourage conservation and support ecosystem services. Urban Ecosyst 2022. [DOI: 10.1007/s11252-022-01241-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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11
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Ulichney V, Jarcho JM, Shipley TF, Ham J, Helion C. Social comparison for concern and action on climate change, racial injustice, and COVID-19. ANALYSES OF SOCIAL ISSUES AND PUBLIC POLICY : ASAP 2022; 22:ASAP12309. [PMID: 35602991 PMCID: PMC9111435 DOI: 10.1111/asap.12309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 01/24/2022] [Accepted: 02/25/2022] [Indexed: 06/15/2023]
Abstract
Preventing the negative impacts of major, intersectional social issues hinges on personal concern and willingness to take action. This research examines social comparison in the context of climate change, racial injustice, and COVID-19 during Fall 2020. Participants in a U.S. university sample (n = 288), reported personal levels of concern and action and estimated peers' concern and action regarding these three issues. Participants estimated that they were more concerned than peers for all three issues and took more action than peers regarding COVID-19 and climate change. Participants who reported higher levels of personal concern also estimated that they took greater action than peers (relative to participants who reported lower levels of concern). Exploratory analyses found that perceived personal control over social issues were associated with greater concern and action for racial injustice and climate change but not for COVID-19. This indicates that issue-specific features, including perceived controllability, may drive people to differently assess their experiences of distinct social issues.
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Affiliation(s)
| | | | | | - Joy Ham
- Department of Psychology, Temple UniversityPhiladelphiaPAUSA
| | - Chelsea Helion
- Department of Psychology, Temple UniversityPhiladelphiaPAUSA
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12
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UACD: A Local Approach for Identifying the Most Influential Spreaders in Twitter in a Distributed Environment. SOCIAL NETWORK ANALYSIS AND MINING 2022. [DOI: 10.1007/s13278-022-00862-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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13
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Tang J, Yu G, Yao X. Emotional Contagion in the Online Depression Community. Healthcare (Basel) 2021; 9:1609. [PMID: 34946335 PMCID: PMC8700837 DOI: 10.3390/healthcare9121609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/19/2021] [Accepted: 11/20/2021] [Indexed: 11/23/2022] Open
Abstract
Negative emotions are prevalent in the online depression community (ODC), which potentially puts members at risk, according to the theory of emotional contagion. However, emotional contagion in the ODC has not been confirmed. The generalized estimating equation (GEE) was used to verify the extent of emotional contagion using data from 1548 sample users in China's popular ODC. During interaction, the emotional themes were analyzed according to language use. The diurnal patterns of the interaction behaviors were also analyzed. We identified the susceptible groups and analyzed their characteristics. The results confirmed the occurrence of emotional contagion in ODC, that is, the extent to which the user's emotion was affected by the received emotion. Our study also found that when positive emotional contagion occurred, the replies contained more hopefulness, and when negative emotional contagion occurred, the replies contained more hopelessness and fear. Second, positive emotions were easier to spread, and people with higher activity in ODC were more susceptible. In addition, nighttime was an active period for user interaction. The results can help community managers and support groups take measures to promote the spread of positive emotions and reduce the spread of negative emotions.
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Affiliation(s)
| | - Guang Yu
- School of Management, Harbin Institute of Technology, 92 Xidazhi Street, Nangang District, Harbin 150001, China; (J.T.); (X.Y.)
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14
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Role-Aware Information Spread in Online Social Networks. ENTROPY 2021; 23:e23111542. [PMID: 34828240 PMCID: PMC8618065 DOI: 10.3390/e23111542] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 11/10/2021] [Accepted: 11/15/2021] [Indexed: 12/29/2022]
Abstract
Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.
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15
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Theoretical and computational characterizations of interaction mechanisms on Facebook dynamics using a common knowledge model. SOCIAL NETWORK ANALYSIS AND MINING 2021. [DOI: 10.1007/s13278-021-00791-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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16
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Wagner LM, Clifton SM. Modeling the public health impact of e-cigarettes on adolescents and adults. CHAOS (WOODBURY, N.Y.) 2021; 31:113137. [PMID: 34881588 DOI: 10.1063/5.0063593] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 10/20/2021] [Indexed: 06/13/2023]
Abstract
Since the introduction of electronic cigarettes to the U.S. market in 2007, vaping prevalence has surged in both adult and adolescent populations. E-cigarettes are advertised as a safer alternative to traditional cigarettes and as a method of smoking cessation, but the U.S. government and health professionals are concerned that e-cigarettes attract young non-smokers. Here, we develop and analyze a dynamical systems model of competition between traditional and electronic cigarettes for users. With this model, we predict the change in smoking prevalence due to the introduction of vaping, and we determine the conditions under which e-cigarettes present a net public health benefit or harm to society.
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Affiliation(s)
- Lucia M Wagner
- Department of Mathematics, Statistics, and Computer Science, St. Olaf College, Northfield, Minnesota 55057, USA
| | - Sara M Clifton
- Department of Mathematics, Statistics, and Computer Science, St. Olaf College, Northfield, Minnesota 55057, USA
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Donges JF, Lochner JH, Kitzmann NH, Heitzig J, Lehmann S, Wiedermann M, Vollmer J. Dose-response functions and surrogate models for exploring social contagion in the Copenhagen Networks Study. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS 2021; 230:3311-3334. [PMID: 34611486 PMCID: PMC8484857 DOI: 10.1140/epjs/s11734-021-00279-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 09/03/2021] [Indexed: 06/13/2023]
Abstract
Spreading dynamics and complex contagion processes on networks are important mechanisms underlying the emergence of critical transitions, tipping points and other non-linear phenomena in complex human and natural systems. Increasing amounts of temporal network data are now becoming available to study such spreading processes of behaviours, opinions, ideas, diseases and innovations to test hypotheses regarding their specific properties. To this end, we here present a methodology based on dose-response functions and hypothesis testing using surrogate data models that randomise most aspects of the empirical data while conserving certain structures relevant to contagion, group or homophily dynamics. We demonstrate this methodology for synthetic temporal network data of spreading processes generated by the adaptive voter model. Furthermore, we apply it to empirical temporal network data from the Copenhagen Networks Study. This data set provides a physically-close-contact network between several hundreds of university students participating in the study over the course of 3 months. We study the potential spreading dynamics of the health-related behaviour "regularly going to the fitness studio" on this network. Based on a hierarchy of surrogate data models, we find that our method neither provides significant evidence for an influence of a dose-response-type network spreading process in this data set, nor significant evidence for homophily. The empirical dynamics in exercise behaviour are likely better described by individual features such as the disposition towards the behaviour, and the persistence to maintain it, as well as external influences affecting the whole group, and the non-trivial network structure. The proposed methodology is generic and promising also for applications to other temporal network data sets and traits of interest.
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Affiliation(s)
- Jonathan F. Donges
- Earth System Analysis and Complexity Science, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
- Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
| | - Jakob H. Lochner
- Earth System Analysis and Complexity Science, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
- Institute for Theoretical Physics, University of Leipzig, Leipzig, Germany
| | - Niklas H. Kitzmann
- Earth System Analysis and Complexity Science, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
- Institute for Physics and Astronomy, University of Potsdam, Potsdam, Germany
| | - Jobst Heitzig
- Earth System Analysis and Complexity Science, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
| | - Sune Lehmann
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Center for Social Data Science, University of Copenhagen, Copenhagen, Denmark
| | - Marc Wiedermann
- Earth System Analysis and Complexity Science, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
- Robert Koch-Institut, Berlin, Germany
- Institute for Theoretical Biology, Humboldt University of Berlin, Berlin, Germany
| | - Jürgen Vollmer
- Institute for Theoretical Physics, University of Leipzig, Leipzig, Germany
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18
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Houli D, Radford ML, Singh VK. “
COVID19
is_”: The Perpetuation of Coronavirus Conspiracy Theories via Google Autocomplete. PROCEEDINGS OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY 2021; 58:218-229. [PMID: 34901396 PMCID: PMC8646906 DOI: 10.1002/pra2.450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
As the impact of the COVID‐19 pandemic grew in 2020, uncertainty surrounding its origins and nature led to widespread conspiracy‐related theories (CRT). Use of technological platforms enabled the rapid and exponential dissemination of COVID‐19 CRT. This study applies social contagion theory to examine how Google Autocomplete (GA) propagates and perpetuates these CRT. An in‐house software program, Autocomplete Search Logging Tool (ASLT) captured a snapshot of GA COVID‐19 related searches early in the pandemic (from March to May 2020) across 76 randomly‐selected countries to gain insight into search behaviors around the world. Analysis identified 15 keywords relating to COVID‐19 CRT predictions and demonstrate how searches across different countries received varying degrees of GA predictions. When grouped with similar keywords, two major categories were identified “Man‐Made Biological Weapon” (42%, n = 2,111), and “Questioning Reality/Severity of COVID‐19” (44%, n = 2,224). This investigation is also among the first to apply social contagion theory to autocomplete applications and can be used in future research to explain and perhaps mitigate the spread of CRT.
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19
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Liang H. Decreasing social contagion effects in diffusion cascades: Modeling message spreading on social media. TELEMATICS AND INFORMATICS 2021. [DOI: 10.1016/j.tele.2021.101623] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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20
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Samson L, Buijzen M. How media appeals depicting social eating contexts increase the appetitive motivational processing of healthy foods. Appetite 2021; 167:105582. [PMID: 34245801 DOI: 10.1016/j.appet.2021.105582] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 05/26/2021] [Accepted: 06/28/2021] [Indexed: 11/24/2022]
Abstract
Research suggests that depictions of social groups can improve the processing of pronutritional media promoting healthy foods. Drawing on a framework of motivational processing, which regulates the automatic emotional and attentional responses to stimuli with adaptive significance to the organism (Cacioppo, Gardner, & Berntson, 1999; Compton, 2003; Ito, Cacioppo, & Lang, 1998), two mixed-factorial experiments examined how adolescents process pronutritional media depicting various social versus alone eating contexts. Based on motivational theories of information processing and emotional contagion, we predicted that pronutritional media depicting social eating contexts capture attention, emotion, and memory formation, indicative of appetitive motivational processing. Study 1 (N = 58; aged 12-18; 54% female) examined how the depicted social eating contexts improve the processing of pronutritional media by increasing their attentional selection, attentional processing, the emotional affect, and arousal responses to them. As the models' faces-which automatically attract priority processing-are oriented towards the foods in the social eating contexts, the pronutritional images depicting social eating contexts were predicted to attract greater attention and mental resources, and to further direct them to the foods. Study 2 (N = 165; aged 12-18; 53% female) investigated how the depicted social eating contexts further improve the processing of the healthy foods in the pronutritional media, by directing the visual attentional focus to the foods and attracting memory formation for them. Visual attentional focus was assessed through eye-tracking and memory was operationalized via visual recognition. As hypothesized, healthy foods became noticeable, highly-arousing, and memorable stimuli with adaptive significance to the organism when promoted through depictions of shared meals in social groups. The findings illustrate how healthy foods can be promoted more effectively through depictions of social eating contexts, and how the appetitive motivational processing explicates their greater effectiveness.
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Affiliation(s)
- Lelia Samson
- School of Journalism and Communication, Renmin University of China, 59 Zhongguancun St., Haidian District, Beijing, 100872, China.
| | - Moniek Buijzen
- Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062, PA, Rotterdam, the Netherlands.
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Burton JW, Cruz N, Hahn U. Reconsidering evidence of moral contagion in online social networks. Nat Hum Behav 2021; 5:1629-1635. [PMID: 34112981 DOI: 10.1038/s41562-021-01133-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 05/04/2021] [Indexed: 11/09/2022]
Abstract
The ubiquity of social media use and the digital data traces it produces has triggered a potential methodological shift in the psychological sciences away from traditional, laboratory-based experimentation. The hope is that, by using computational social science methods to analyse large-scale observational data from social media, human behaviour can be studied with greater statistical power and ecological validity. However, current standards of null hypothesis significance testing and correlational statistics seem ill-suited to markedly noisy, high-dimensional social media datasets. We explore this point by probing the moral contagion phenomenon, whereby the use of moral-emotional language increases the probability of message spread. Through out-of-sample prediction, model comparisons and specification curve analyses, we find that the moral contagion model performs no better than an implausible XYZ contagion model. This highlights the risks of using purely correlational evidence from large observational datasets and sounds a cautionary note for psychology's merge with big data.
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Affiliation(s)
- Jason W Burton
- Department of Psychological Sciences, Birkbeck, University of London, London, UK.
| | - Nicole Cruz
- School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
| | - Ulrike Hahn
- Department of Psychological Sciences, Birkbeck, University of London, London, UK
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22
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Do Data from Large Personal Networks Support Cultural Evolutionary Ideas about Kin and Fertility? SOCIAL SCIENCES 2021. [DOI: 10.3390/socsci10050177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The fertility decline associated with economic development has been attributed to a host of interrelated causes including the rising costs of children with industrialization, and shifts in family structure. One hypothesis is that kin may impart more pro-natal information within their networks than non-kin, and that this effect may be exacerbated in networks with high kin-density where greater social conformity would be expected. In this study, we tested these ideas using large personal networks (25 associates of the respondent) collected from a sample of Dutch women (N = 706). Kin (parents) were perceived to exert slightly more social pressure to have children than non-kin, although dense networks were not associated with greater pressure. In contrast, women reported talking to friends about having children to a greater extent than kin, although greater kin-density in the network increased the likelihood of women reporting that they could talk to kin about having children. Both consanguineal and affinal kin could be asked to help with child-care to a greater extent than friends and other non-kin. Overall, there was mixed evidence that kin were more likely to offer pro-natal information than non-kin, and better evidence to suggest that kin were considered to be a better source of child-care support.
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23
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Seaman A, Leichtling G, Stack E, Gray M, Pope J, Larsen JE, Leahy JM, Gelberg L, Korthuis PT. Harm Reduction and Adaptations Among PWUD in Rural Oregon During COVID-19. AIDS Behav 2021; 25:1331-1339. [PMID: 33471243 PMCID: PMC7816753 DOI: 10.1007/s10461-020-03141-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/15/2020] [Indexed: 11/29/2022]
Abstract
Coronavirus Disease 2019 (COVID-19) may influence HIV/HCV transmission risk behaviors in rural communities. We conducted semi-structured qualitative interviews with people who use drugs (PWUD) in five rural Oregon counties and asked about COVID-19 impact on substance use and harm reduction practices and their advice for improving public health responses. Participants (n = 36) reported using only methamphetamine (52.8%), only heroin (16.7%), or both (30.6%); 75% of participants reported recent injection. Three thematic categories emerged: SSP adaptations and accessibility, PWUD harm reduction practices, and policy suggestions. Participants noted the importance of SSPs to COVID-19 prevention and wellbeing, though some experienced increased barriers, leading to increased risky injection practices. Participants suggested need-based rather than one-for-one exchange, increasing syringe delivery services, encouraging secondary exchange by PWUD, and peers as trusted voices for information exchange. Rapid implementation of policy and practice changes are urgently required to improve SSP access, reinforce safer use, and prevent HIV/HCV and COVID-19 transmission.
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Affiliation(s)
- Andrew Seaman
- Section of Addiction Medicine, Department of Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Mail Code - L475, Portland, 97239-3098, OR, USA.
| | | | | | | | | | - Jessica E Larsen
- Section of Addiction Medicine, Department of Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Mail Code - L475, Portland, 97239-3098, OR, USA
| | - Judith M Leahy
- Oregon Health Authority, Acute and Communicable Disease Prevention, Public Health Division, Oregon Health Authority, Salem, OR, USA
| | - Lillian Gelberg
- Oregon Health Authority, Acute and Communicable Disease Prevention, Public Health Division, Oregon Health Authority, Salem, OR, USA
- Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, USA
| | - P Todd Korthuis
- Section of Addiction Medicine, Department of Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Mail Code - L475, Portland, 97239-3098, OR, USA
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24
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Emmert-Streib F, Dehmer M. Data-Driven Computational Social Network Science: Predictive and Inferential Models for Web-Enabled Scientific Discoveries. Front Big Data 2021; 4:591749. [PMID: 33969290 PMCID: PMC8100320 DOI: 10.3389/fdata.2021.591749] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 02/18/2021] [Indexed: 12/13/2022] Open
Abstract
The ultimate goal of the social sciences is to find a general social theory encompassing all aspects of social and collective phenomena. The traditional approach to this is very stringent by trying to find causal explanations and models. However, this approach has been recently criticized for preventing progress due to neglecting prediction abilities of models that support more problem-oriented approaches. The latter models would be enabled by the surge of big Web-data currently available. Interestingly, this problem cannot be overcome with methods from computational social science (CSS) alone because this field is dominated by simulation-based approaches and descriptive models. In this article, we address this issue and argue that the combination of big social data with social networks is needed for creating prediction models. We will argue that this alliance has the potential for gradually establishing a causal social theory. In order to emphasize the importance of integrating big social data with social networks, we call this approach data-driven computational social network science (DD-CSNS).
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Affiliation(s)
- Frank Emmert-Streib
- Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.,Institute of Biosciences and Medical Technology, Tampere, Finland
| | - Matthias Dehmer
- Department of Computer Science, Swiss Distance University of Applied Sciences, Brig, Switzerland.,School of Science, Xian Technological University, Xian, China.,College of Artificial Intelligence, Nankai University, Tianjin, China.,Department of Biomedical Computer Science and Mechatronics, The Health and Life Science University, UMIT, Hall in Tyrol, Austria
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25
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Alshaabi T, Dewhurst DR, Minot JR, Arnold MV, Adams JL, Danforth CM, Dodds PS. The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009-2020. EPJ DATA SCIENCE 2021; 10:15. [PMID: 33816048 PMCID: PMC8010293 DOI: 10.1140/epjds/s13688-021-00271-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 03/17/2021] [Indexed: 06/12/2023]
Abstract
Working from a dataset of 118 billion messages running from the start of 2009 to the end of 2019, we identify and explore the relative daily use of over 150 languages on Twitter. We find that eight languages comprise 80% of all tweets, with English, Japanese, Spanish, Arabic, and Portuguese being the most dominant. To quantify social spreading in each language over time, we compute the 'contagion ratio': The balance of retweets to organic messages. We find that for the most common languages on Twitter there is a growing tendency, though not universal, to retweet rather than share new content. By the end of 2019, the contagion ratios for half of the top 30 languages, including English and Spanish, had reached above 1-the naive contagion threshold. In 2019, the top 5 languages with the highest average daily ratios were, in order, Thai (7.3), Hindi, Tamil, Urdu, and Catalan, while the bottom 5 were Russian, Swedish, Esperanto, Cebuano, and Finnish (0.26). Further, we show that over time, the contagion ratios for most common languages are growing more strongly than those of rare languages.
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Affiliation(s)
- Thayer Alshaabi
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405 USA
- Computational Story Lab, University of Vermont, Burlington, VT 05405 USA
- Department of Computer Science, University of Vermont, Burlington, VT 05405 USA
| | - David Rushing Dewhurst
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405 USA
- Computational Story Lab, University of Vermont, Burlington, VT 05405 USA
- Charles River Analytics, Cambridge, MA 02138 USA
| | - Joshua R. Minot
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405 USA
- Computational Story Lab, University of Vermont, Burlington, VT 05405 USA
| | - Michael V. Arnold
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405 USA
- Computational Story Lab, University of Vermont, Burlington, VT 05405 USA
| | - Jane L. Adams
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405 USA
- Computational Story Lab, University of Vermont, Burlington, VT 05405 USA
| | - Christopher M. Danforth
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405 USA
- Computational Story Lab, University of Vermont, Burlington, VT 05405 USA
- Department of Mathematics & Statistics, University of Vermont, Burlington, VT 05405 USA
| | - Peter Sheridan Dodds
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405 USA
- Computational Story Lab, University of Vermont, Burlington, VT 05405 USA
- Department of Computer Science, University of Vermont, Burlington, VT 05405 USA
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26
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Inferring mechanisms of response prioritization on social media under information overload. Sci Rep 2021; 11:1346. [PMID: 33446767 PMCID: PMC7809357 DOI: 10.1038/s41598-020-79897-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 12/14/2020] [Indexed: 11/08/2022] Open
Abstract
Human decision-making is subject to the biological limits of cognition. The fluidity of information propagation over online social media often leads users to experience information overload. This in turn affects which information received by users are processed and gain a response to, imposing constraints on volumes of, and participation in, information cascades. In this study, we investigate properties contributing to the visibility of online social media notifications by highly active users experiencing information overload via cross-platform social influence. We analyze simulations of a coupled agent-based model of information overload and the multi-action cascade model of conversation with evolutionary model discovery. Evolutionary model discovery automates mechanistic inference on agent-based models by enabling random forest importance analysis on genetically programmed agent-based model rules. The mechanisms of information overload have shown to contribute to a multitude of global properties of online information cascades. We investigate nine characteristics of online messages that may contribute to the prioritization of messages for response. Our results indicate that recency had the largest contribution to message visibility, with individuals prioritizing more recent notifications. Global popularity of the conversation originator had the second highest contribution, and reduced message visibility. Messages that presented opportunity for novel user interaction, yet high reciprocity showed to have relatively moderate contribution to message visibility. Finally, insights from the evolutionary model discovery results helped inform response prioritization rules, which improved the robustness and accuracy of the model of information overload.
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27
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Evans JC, Silk MJ, Boogert NJ, Hodgson DJ. Infected or informed? Social structure and the simultaneous transmission of information and infectious disease. OIKOS 2020. [DOI: 10.1111/oik.07148] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Julian C. Evans
- Dept of Evolutionary Biology and Environmental Studies, Univ. of Zurich Switzerland
| | - Matthew J. Silk
- Centre for Ecology and Conservation, Univ. of Exeter Penryn Campus UK
- Environment and Sustainability Inst., Univ. of Exeter Penryn Campus UK
| | | | - David J. Hodgson
- Centre for Ecology and Conservation, Univ. of Exeter Penryn Campus UK
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28
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Bartal A, Pliskin N, Tsur O. Local/Global contagion of viral/non-viral information: Analysis of contagion spread in online social networks. PLoS One 2020; 15:e0230811. [PMID: 32275716 PMCID: PMC7147744 DOI: 10.1371/journal.pone.0230811] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 03/09/2020] [Indexed: 11/30/2022] Open
Abstract
Contagion in online social networks (OSN) occurs when users are exposed to information disseminated by other users. Studies of contagion are largely devoted to the spread of viral information and to local neighbor-to-neighbor contagion. However, many contagion events can be non-viral in the sense of being unpopular with low reach size, or global in the sense of being exposed to non-adjacent neighbors. This study aims to investigate the differences between local and global contagion and the different contagion patterns of viral vs. non-viral information. We analyzed three datasets and found significant differences between the temporal spreading patterns of local contagion compared to global contagion. Based on our analysis, we can successfully predict whether a user will be infected by either a local or a global contagion. We achieve an F1-score of 0.87 for non-viral information and an F1-score of 0.84 for viral information. We propose a novel method for early detection of the viral potential of an information nugget and investigate the spreading of viral and non-viral information. In addition, we analyze both viral and non-viral contagion of a topic. Differentiating between local versus global contagion, as well as between viral versus non-viral information, provides a novel perspective and better understanding of information diffusion in OSNs.
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Affiliation(s)
- Alon Bartal
- Dept. of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- * E-mail:
| | - Nava Pliskin
- Dept. of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Oren Tsur
- Dept. of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
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29
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Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data. Processes (Basel) 2020. [DOI: 10.3390/pr8020243] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Virtual sensors, or soft sensors, have greatly contributed to the evolution of the sensing systems in industry. The soft sensors are process models having three fundamental categories, namely white-box (WB), black-box (BB) and gray-box (GB) models. WB models are based on process knowledge while the BB models are developed using data collected from the process. The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators. In this work, various design aspects of the GB models are discussed followed by their application in the process industry. In addition, the changes in the data-driven part of the GB models in the context of enormous amount of process data collected in Industry 4.0 are elaborated.
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30
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Iacopini I, Schäfer B, Arcaute E, Beck C, Latora V. Multilayer modeling of adoption dynamics in energy demand management. CHAOS (WOODBURY, N.Y.) 2020; 30:013153. [PMID: 32013493 DOI: 10.1063/1.5122313] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 01/14/2020] [Indexed: 06/10/2023]
Abstract
Due to the emergence of new technologies, the whole electricity system is undergoing transformations on a scale and pace never observed before. The decentralization of energy resources and the smart grid have forced utility services to rethink their relationships with customers. Demand response (DR) seeks to adjust the demand for power instead of adjusting the supply. However, DR business models rely on customer participation and can only be effective when large numbers of customers in close geographic vicinity, e.g., connected to the same transformer, opt in. Here, we introduce a model for the dynamics of service adoption on a two-layer multiplex network: the layer of social interactions among customers and the power-grid layer connecting the households. While the adoption process-based on peer-to-peer communication-runs on the social layer, the time-dependent recovery rate of the nodes depends on the states of their neighbors on the power-grid layer, making an infected node surrounded by infectious ones less keen to recover. Numerical simulations of the model on synthetic and real-world networks show that a strong local influence of the customers' actions leads to a discontinuous transition where either none or all the nodes in the network are infected, depending on the infection rate and social pressure to adopt. We find that clusters of early adopters act as points of high local pressure, helping maintaining adopters, and facilitating the eventual adoption of all nodes. This suggests direct marketing strategies on how to efficiently establish and maintain new technologies such as DR schemes.
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Affiliation(s)
- Iacopo Iacopini
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
| | - Benjamin Schäfer
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
| | - Elsa Arcaute
- Centre for Advanced Spatial Analysis, University College London, London W1T 4TJ, United Kingdom
| | - Christian Beck
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
| | - Vito Latora
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
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31
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Cai H, Nguyen TT, Li Y, Zheng VW, Chen B, Cong G, Li X. Modeling Marked Temporal Point Process Using Multi-relation Structure RNN. Cognit Comput 2019. [DOI: 10.1007/s12559-019-09690-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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32
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Yoo E, Gu B, Rabinovich E. Diffusion on Social Media Platforms: A Point Process Model for Interaction among Similar Content. J MANAGE INFORM SYST 2019. [DOI: 10.1080/07421222.2019.1661096] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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33
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Peixoto TP. Network Reconstruction and Community Detection from Dynamics. PHYSICAL REVIEW LETTERS 2019; 123:128301. [PMID: 31633974 PMCID: PMC7226905 DOI: 10.1103/physrevlett.123.128301] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 05/21/2019] [Indexed: 05/06/2023]
Abstract
We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network. We show that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform the existence of communities are also inherently used to improve the accuracy of the reconstruction which, in turn, can better inform the uncovering of communities. We illustrate the use of our method with observations arising from epidemic models and the Ising model, both on synthetic and empirical networks, as well as on data containing only functional information.
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Affiliation(s)
- Tiago P Peixoto
- Department of Network and Data Science, Central European University, H-1051 Budapest, Hungary
- ISI Foundation, Via Chisola 5, 10126 Torino, Italy
- Department of Mathematical Sciences, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom
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34
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35
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36
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Smolla M, Akçay E. Cultural selection shapes network structure. SCIENCE ADVANCES 2019; 5:eaaw0609. [PMID: 31453324 PMCID: PMC6693906 DOI: 10.1126/sciadv.aaw0609] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 07/10/2019] [Indexed: 05/03/2023]
Abstract
Cultural evolution relies on the social transmission of cultural traits along a population's social network. Research indicates that network structure affects information spread and thus the capacity for cumulative culture. However, how network structure itself is driven by population-culture co-evolution remains largely unclear. We use a simple model to investigate how populations negotiate the trade-off between acquiring new skills and getting better at existing skills and how this trade-off shapes social networks. We find unexpected eco-evolutionary feedbacks from culture onto social networks and vice versa. We show that selecting for skill generalists results in sparse networks with diverse skill sets, whereas selecting for skill specialists results in dense networks and a population that specializes on the same few skills on which everyone is an expert. Our model advances our understanding of the complex feedbacks in cultural evolution and demonstrates how individual-level behavior can lead to the emergence of population-level structure.
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Affiliation(s)
- Marco Smolla
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
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37
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Iacopini I, Petri G, Barrat A, Latora V. Simplicial models of social contagion. Nat Commun 2019; 10:2485. [PMID: 31171784 PMCID: PMC6554271 DOI: 10.1038/s41467-019-10431-6] [Citation(s) in RCA: 134] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 05/03/2019] [Indexed: 11/24/2022] Open
Abstract
Complex networks have been successfully used to describe the spread of diseases in populations of interacting individuals. Conversely, pairwise interactions are often not enough to characterize social contagion processes such as opinion formation or the adoption of novelties, where complex mechanisms of influence and reinforcement are at work. Here we introduce a higher-order model of social contagion in which a social system is represented by a simplicial complex and contagion can occur through interactions in groups of different sizes. Numerical simulations of the model on both empirical and synthetic simplicial complexes highlight the emergence of novel phenomena such as a discontinuous transition induced by higher-order interactions. We show analytically that the transition is discontinuous and that a bistable region appears where healthy and endemic states co-exist. Our results help explain why critical masses are required to initiate social changes and contribute to the understanding of higher-order interactions in complex systems.
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Affiliation(s)
- Iacopo Iacopini
- School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, UK
- The Alan Turing Institute, The British Library, London, NW1 2DB, UK
| | - Giovanni Petri
- ISI Foundation, Via Chisola 5, 10126, Turin, Italy
- ISI Global Science Foundation, 33 W 42nd St, New York, NY, 10036, USA
| | - Alain Barrat
- ISI Foundation, Via Chisola 5, 10126, Turin, Italy
- Aix Marseille Univ, Université de Toulon, CNRS, CPT, Marseille, 13009, France
| | - Vito Latora
- School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, UK.
- The Alan Turing Institute, The British Library, London, NW1 2DB, UK.
- Dipartimento di Fisica ed Astronomia, Universitá di Catania and INFN, 95123, Catania, Italy.
- Complexity Science Hub Vienna, Josefstädter Strasse 39, Vienna, 1080, Austria.
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38
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Close and ordinary social contacts: How important are they in promoting large-scale contagion? Phys Rev E 2018; 98:052311. [PMCID: PMC7217557 DOI: 10.1103/physreve.98.052311] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
An outstanding problem of interdisciplinary interest is to understand quantitatively the role of social contacts in contagion dynamics. In general, there are two types of contacts: close ones among friends, colleagues and family members, etc., and ordinary contacts from encounters with strangers. Typically, social reinforcement occurs for close contacts. Taking into account both types of contacts, we develop a contact-based model for social contagion. We find that, associated with the spreading dynamics, for random networks there is coexistence of continuous and discontinuous phase transitions, but for heterogeneous networks the transition is continuous. We also find that ordinary contacts play a crucial role in promoting large-scale spreading, and the number of close contacts determines not only the nature of the phase transitions but also the value of the outbreak threshold in random networks. For heterogeneous networks from the real world, the abundance of close contacts affects the epidemic threshold, while its role in facilitating the spreading depends on the adoption threshold assigned to it. We uncover two striking phenomena. First, a strong interplay between ordinary and close contacts is necessary for generating prevalent spreading. In fact, only when there are propagation paths of reasonable length which involve both close and ordinary contacts are large-scale outbreaks of social contagions possible. Second, abundant close contacts in heterogeneous networks promote both outbreak and spreading of the contagion through the transmission channels among the hubs, when both values of the threshold and transmission rate among ordinary contacts are small. We develop a theoretical framework to obtain an analytic understanding of the main findings on random networks, with support from extensive numerical computations. Overall, ordinary contacts facilitate spreading over the entire network, while close contacts determine the way by which outbreaks occur, i.e., through a second- or first-order phase transition. These results provide quantitative insights into how certain social behaviors can emerge and become prevalent, which has potential implications not only to social science but also to economics and political science.
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39
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Irresponsible Research and Innovation? Applying Findings from Neuroscience to Analysis of Unsustainable Hype Cycles. SUSTAINABILITY 2018. [DOI: 10.3390/su10103472] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The introduction of technological innovations is often associated with suboptimal decisions and actions during cycles of inflated expectations, disappointment, and unintended negative consequences. For brevity, these can be referred to as hype cycles. Hitherto, studies have reported hype cycles for many different technologies, and studies have proposed different methods for improving the introduction of technological innovations. Yet hype cycles persist, despite suboptimal outcomes being widely reported and despite methods being available to improve outcomes. In this communication paper, findings from exploratory research are reported, which introduce new directions for addressing hype cycles. Through reference to neuroscience studies, it is explained that the behavior of some adults in hype cycles can be analogous to that of irresponsible behavior among adolescents. In particular, there is heightened responsiveness to peer presence and potential rewards. Accordingly, it is argued that methods applied successfully to reduce irresponsible behavior among adolescents are relevant to addressing hype cycles, and to facilitating more responsible research and innovation. The unsustainability of hype cycles is considered in relation to hype about artificial intelligence (AI). In particular, the potential for human-beneficial AI to have the unintended negative consequence of being fatally unbeneficial to everything else in the geosphere other than human beings.
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40
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Lin Y, Burghardt K, Rohden M, Noël PA, D'Souza RM. Self-organization of dragon king failures. Phys Rev E 2018; 98:022127. [PMID: 30253566 DOI: 10.1103/physreve.98.022127] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Indexed: 11/07/2022]
Abstract
The mechanisms underlying cascading failures are often modeled via the paradigm of self-organized criticality. Here we introduce a simple network model where nodes self-organize to be either weakly or strongly protected against failure in a manner that captures the trade-off between degradation and reinforcement of nodes inherent in many network systems. If strong nodes cannot fail, any failure is contained to a single, isolated cluster of weak nodes and the model produces power-law distributions of failure sizes. We classify the large, rare events that involve the failure of only a single cluster as "black swans." In contrast, if strong nodes fail once a sufficient fraction of their neighbors fail, then failure can cascade across multiple clusters of weak nodes. If over 99.9% of the nodes fail due to this cluster hopping mechanism, we classify this as a "dragon king," which are massive failures caused by mechanisms distinct from smaller failures. The dragon kings observed are self-organized, existing over a wide range of reinforcement rates and system sizes. We find that once an initial cluster of failing weak nodes is above a critical size, the dragon king mechanism kicks in, leading to piggybacking system-wide failures. We demonstrate that the size of the initial failed weak cluster predicts the likelihood of a dragon king event with high accuracy and we develop a simple control strategy that can dramatically reduce dragon kings and other large failures.
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Affiliation(s)
- Yuansheng Lin
- School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.,Beijing Jingdong Century Trade Co., Ltd., Beijing 101111, China.,Department of Computer Science, University of California, Davis, California 95616, USA
| | - Keith Burghardt
- Information Sciences Institute, University of Southern California, Marina del Rey, California 90292, USA
| | - Martin Rohden
- Department of Computer Science, University of California, Davis, California 95616, USA
| | - Pierre-André Noël
- Department of Computer Science, University of California, Davis, California 95616, USA
| | - Raissa M D'Souza
- Department of Computer Science, University of California, Davis, California 95616, USA.,Department of Mechanical and Aerospace Engineering, University of California, Davis, California 95616, USA.,Santa Fe Institute, Santa Fe, New Mexico 87501, USA
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Min B, San Miguel M. Competing contagion processes: Complex contagion triggered by simple contagion. Sci Rep 2018; 8:10422. [PMID: 29991815 PMCID: PMC6039514 DOI: 10.1038/s41598-018-28615-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 06/26/2018] [Indexed: 11/08/2022] Open
Abstract
Empirical evidence reveals that contagion processes often occur with competition of simple and complex contagion, meaning that while some agents follow simple contagion, others follow complex contagion. Simple contagion refers to spreading processes induced by a single exposure to a contagious entity while complex contagion demands multiple exposures for transmission. Inspired by this observation, we propose a model of contagion dynamics with a transmission probability that initiates a process of complex contagion. With this probability nodes subject to simple contagion get adopted and trigger a process of complex contagion. We obtain a phase diagram in the parameter space of the transmission probability and the fraction of nodes subject to complex contagion. Our contagion model exhibits a rich variety of phase transitions such as continuous, discontinuous, and hybrid phase transitions, criticality, tricriticality, and double transitions. In particular, we find a double phase transition showing a continuous transition and a following discontinuous transition in the density of adopted nodes with respect to the transmission probability. We show that the double transition occurs with an intermediate phase in which nodes following simple contagion become adopted but nodes with complex contagion remain susceptible.
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Affiliation(s)
- Byungjoon Min
- IFISC, Instituto de Física Interdisciplinar y Sistemas Complejos (CSIC-UIB), Campus Universitat Illes Balears, E-07122, Palma de Mallorca, Spain.
- Department of Physics, Chungbuk National University, Cheongju, Chungbuk, 28644, Korea.
| | - Maxi San Miguel
- IFISC, Instituto de Física Interdisciplinar y Sistemas Complejos (CSIC-UIB), Campus Universitat Illes Balears, E-07122, Palma de Mallorca, Spain.
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Liu QH, Lü FM, Zhang Q, Tang M, Zhou T. Impacts of opinion leaders on social contagions. CHAOS (WOODBURY, N.Y.) 2018; 28:053103. [PMID: 29857688 DOI: 10.1063/1.5017515] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Opinion leaders are ubiquitous in both online and offline social networks, but the impacts of opinion leaders on social behavior contagions are still not fully understood, especially by using a mathematical model. Here, we generalize the classical Watts threshold model and address the influences of the opinion leaders, where an individual adopts a new behavior if one of his/her opinion leaders adopts the behavior. First, we choose the opinion leaders randomly from all individuals in the network and find that the impacts of opinion leaders make other individuals adopt the behavior more easily. Specifically, the existence of opinion leaders reduces the lowest mean degree of the network required for the global behavior adoption and increases the highest mean degree of the network that the global behavior adoption can occur. Besides, the introduction of opinion leaders accelerates the behavior adoption but does not change the adoption order of individuals. The developed theoretical predictions agree with the simulation results. Second, we randomly choose the opinion leaders from the top h% of the highest degree individuals and find an optimal h% for the network with the lowest mean degree that the global behavior adoption can occur. Meanwhile, the influences of opinion leaders on accelerating the adoption of behaviors become less significant and can even be ignored when reducing the value of h%.
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Affiliation(s)
- Quan-Hui Liu
- Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Feng-Mao Lü
- Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qian Zhang
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts 02115, USA
| | - Ming Tang
- Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Tao Zhou
- Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
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Abstract
The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and widely scattered across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges 'spontaneously' in the absence of centralized institutions and covers topics that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.
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Mønsted B, Sapieżyński P, Ferrara E, Lehmann S. Evidence of complex contagion of information in social media: An experiment using Twitter bots. PLoS One 2017; 12:e0184148. [PMID: 28937984 PMCID: PMC5609738 DOI: 10.1371/journal.pone.0184148] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 08/18/2017] [Indexed: 11/26/2022] Open
Abstract
It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using ‘social bots’ deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.
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Affiliation(s)
- Bjarke Mønsted
- Technical University of Denmark, Applied Mathematics and Computer Science, 2800 Lyngby, Denmark
| | - Piotr Sapieżyński
- Technical University of Denmark, Applied Mathematics and Computer Science, 2800 Lyngby, Denmark
| | - Emilio Ferrara
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292, United States of America
- Indiana University, Network Science Institute, Bloomington, IN, United States of America
| | - Sune Lehmann
- Technical University of Denmark, Applied Mathematics and Computer Science, 2800 Lyngby, Denmark
- * E-mail:
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46
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Mateo D, Kuan YK, Bouffanais R. Effect of Correlations in Swarms on Collective Response. Sci Rep 2017; 7:10388. [PMID: 28871122 PMCID: PMC5583190 DOI: 10.1038/s41598-017-09830-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 07/31/2017] [Indexed: 11/09/2022] Open
Abstract
Social interaction increases significantly the performance of a wide range of cooperative systems. However, evidence that natural swarms limit the number of interactions suggests potentially detrimental consequences of excessive interaction. Using a canonical model of collective motion, we find that the collective response to a dynamic localized perturbation-emulating a predator attack-is hindered when the number of interacting neighbors exceeds a certain threshold. Specifically, the effectiveness in avoiding the predator is enhanced by large integrated correlations, which are known to peak at a given level of interagent interaction. From the network-theoretic perspective, we uncover the same interplay between number of connections and effectiveness in group-level response for two distinct decision-making models of distributed consensus operating over a range of static networks. The effect of the number of connections on the collective response critically depends on the dynamics of the perturbation. While adding more connections improves the response to slow perturbations, the opposite is true for fast ones. These results have far-reaching implications for the design of artificial swarms or interaction networks.
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Affiliation(s)
- David Mateo
- Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore.
| | - Yoke Kong Kuan
- Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore
| | - Roland Bouffanais
- Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore
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Cottica A, Melançon G, Renoust B. Online community management as social network design: testing for the signature of management activities in online communities. APPLIED NETWORK SCIENCE 2017; 2:30. [PMID: 30443584 PMCID: PMC6214248 DOI: 10.1007/s41109-017-0049-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 08/09/2017] [Indexed: 06/09/2023]
Abstract
Online communities are used across several fields of human activities, as environments for large-scale collaboration. Most successful ones employ professionals, sometimes called "community managers" or "moderators", for tasks including onboarding new participants, mediating conflict, and policing unwanted behaviour. Network scientists routinely model interaction across participants in online communities as social networks. We interpret the activity of community managers as (social) network design: they take action oriented at shaping the network of interactions in a way conducive to their community's goals. It follows that, if such action is successful, we should be able to detect its signature in the network itself. Growing networks where links are allocated by a preferential attachment mechanism are known to converge to networks displaying a power law degree distribution. Growth and preferential attachment are both reasonable first-approximation assumptions to describe interaction networks in online communities. Our main hypothesis is that managed online communities are characterised by in-degree distributions that deviate from the power law form; such deviation constitutes the signature of successful community management. Our secondary hypothesis is that said deviation happens in a predictable way, once community management practices are accounted for. If true, these hypotheses would give us a simple test for the effectiveness of community management practices. We investigate the issue using (1) empirical data on three small online communities and (2) a computer model that simulates a widely used community management activity called onboarding. We find that onboarding produces in-degree distributions that systematically deviate from power law behaviour for low-values of the in-degree; we then explore the implications and possible applications of the finding.
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Affiliation(s)
- Alberto Cottica
- University of Alicante, Alicante, Spain
- Edgeryders, Tallinn, Estonia
| | - Guy Melançon
- Université de Bordeaux, Talence, France
- CNRS UMR 5800 LaBRI, Bordeaux, France
| | - Benjamin Renoust
- National Institute of Informatics, Tokyo, Japan
- CNRS UMI 3527, JFLI, Tokyo, Japan
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Liu QH, Wang W, Tang M, Zhou T, Lai YC. Explosive spreading on complex networks: The role of synergy. Phys Rev E 2017; 95:042320. [PMID: 28505757 DOI: 10.1103/physreve.95.042320] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Indexed: 11/07/2022]
Abstract
In spite of the vast literature on spreading dynamics on complex networks, the role of local synergy, i.e., the interaction of elements that when combined produce a total effect greater than the sum of the individual elements, has been studied but only for irreversible spreading dynamics. Reversible spreading dynamics are ubiquitous but their interplay with synergy has remained unknown. To fill this knowledge gap, we articulate a model to incorporate local synergistic effect into the classical susceptible-infected-susceptible process, in which the probability for a susceptible node to become infected through an infected neighbor is enhanced when the neighborhood of the latter contains a number of infected nodes. We derive master equations incorporating the synergistic effect, with predictions that agree well with the numerical results. A striking finding is that when a parameter characterizing the strength of the synergy reinforcement effect is above a critical value, the steady-state density of the infected nodes versus the basic transmission rate exhibits an explosively increasing behavior and a hysteresis loop emerges. In fact, increasing the synergy strength can promote the spreading and reduce the invasion and persistence thresholds of the hysteresis loop. A physical understanding of the synergy promoting explosive spreading and the associated hysteresis behavior can be obtained through a mean-field analysis.
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Affiliation(s)
- Quan-Hui Liu
- Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.,Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wei Wang
- Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.,Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ming Tang
- Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.,Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Tao Zhou
- Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.,Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ying-Cheng Lai
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA
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Burghardt K, Alsina EF, Girvan M, Rand W, Lerman K. The myopia of crowds: Cognitive load and collective evaluation of answers on Stack Exchange. PLoS One 2017; 12:e0173610. [PMID: 28301531 PMCID: PMC5354439 DOI: 10.1371/journal.pone.0173610] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 02/22/2017] [Indexed: 11/18/2022] Open
Abstract
Crowds can often make better decisions than individuals or small groups of experts by leveraging their ability to aggregate diverse information. Question answering sites, such as Stack Exchange, rely on the “wisdom of crowds” effect to identify the best answers to questions asked by users. We analyze data from 250 communities on the Stack Exchange network to pinpoint factors affecting which answers are chosen as the best answers. Our results suggest that, rather than evaluate all available answers to a question, users rely on simple cognitive heuristics to choose an answer to vote for or accept. These cognitive heuristics are linked to an answer’s salience, such as the order in which it is listed and how much screen space it occupies. While askers appear to depend on heuristics to a greater extent than voters when choosing an answer to accept as the most helpful one, voters use acceptance itself as a heuristic, and they are more likely to choose the answer after it has been accepted than before that answer was accepted. These heuristics become more important in explaining and predicting behavior as the number of available answers to a question increases. Our findings suggest that crowd judgments may become less reliable as the number of answers grows.
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Affiliation(s)
- Keith Burghardt
- Dept of Computer Science, University of California at Davis, Davis, CA, United States of America
- Dept of Political Science, University of California at Davis, Davis, CA, United States of America
- * E-mail:
| | | | - Michelle Girvan
- Dept of Physics, University of Maryland, College Park, MD, United States of America
- Santa Fe Institute, Santa Fe, NM, United States of America
| | - William Rand
- Department of Business Management, North Carolina State University, Raleigh, NC, United States of America
| | - Kristina Lerman
- Information Sciences Institute, University of Southern California, Marina del Rey, CA, United States of America
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50
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Petrič G, Atanasova S, Kamin T. Impact of Social Processes in Online Health Communities on Patient Empowerment in Relationship With the Physician: Emergence of Functional and Dysfunctional Empowerment. J Med Internet Res 2017; 19:e74. [PMID: 28288953 PMCID: PMC5368349 DOI: 10.2196/jmir.7002] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Revised: 02/10/2017] [Accepted: 02/15/2017] [Indexed: 01/10/2023] Open
Abstract
Background Substantial research demonstrates the importance of online health communities (OHCs) for patient empowerment, although the impact on the patient-physician relationship is understudied. Patient empowerment also occurs in relationship with the physician, but studies of OHCs mostly disregard this. The question also remains about the nature and consequences of this empowerment, as it might be based on the limited validity of some information in OHCs. Objective The main purpose of this study was to examine the impact of social processes in OHCs (information exchange with users and health professional moderators, social support, finding meaning, and self-expressing) on functional and dysfunctional patient empowerment in relationship with the physician (PERP). This impact was investigated by taking into account moderating role of eHealth literacy and physician’s paternalism. Method An email list–based Web survey on a simple random sample of 25,000 registered users of the most popular general OHC in Slovenia was conducted. A total of 1572 respondents completed the survey. The analyses were conducted on a subsample of 591 regular users, who had visited a physician at least once in the past 2 years. To estimate the impact of social processes in OHC on functional and dysfunctional PERP, we performed a series of hierarchical regression analyses. To determine the moderating role of eHealth literacy and the perceived physician characteristics, interactions were included in the regression analyses. Results The mean age of the respondents in the sample was 37.6 years (SD 10.3) and 83.3% were females. Factor analyses of the PERP revealed a five-factor structure with acceptable fit (root-mean-square error of approximation =.06). Most important results are that functional self-efficacy is positively predicted by information exchange with health professional moderators (beta=.12, P=.02), information exchange with users (beta=.12, P=.05), and giving social support (beta=.13, P=.02), but negatively predicted with receiving social support (beta=−.21, P<.001). Functional control is also predicted by information exchange with health professional moderators (beta=.16, P=.005). Dysfunctional control and competence are inhibited by information exchanges with health professionals (beta=−.12, P=.03), whereas dysfunctional self-efficacy is inhibited by self-expressing (beta=−.12, P=.05). The process of finding meaning likely leads to the development of dysfunctional competences and control if the physician is perceived to be paternalistic (beta=.14, P=.03). Under the condition of high eHealth literacy, the process of finding meaning will inhibit the development of dysfunctional competences and control (beta=−.17, P=.01). Conclusions Social processes in OHCs do not have a uniform impact on PERP. This impact is moderated by eHealth literacy and physician paternalism. Exchanging information with health professional moderators in OHCs is the most important factor for stimulating functional PERP as well as diminishing dysfunctional PERP. Social support in OHCs plays an ambiguous role, often making patients behave in a strategic, uncooperative way toward physicians.
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Affiliation(s)
- Gregor Petrič
- Centre for Methodology and Informatics, Faculty of Social Sciences, University of Ljubljana, Ljubljana, Slovenia
| | - Sara Atanasova
- Centre for Methodology and Informatics, Faculty of Social Sciences, University of Ljubljana, Ljubljana, Slovenia
| | - Tanja Kamin
- Centre for Social Psychology, Faculty of Social Sciences, University of Ljubljana, Ljubljana, Slovenia
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