Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms

Online social networks are the perfect test bed to better understand large-scale human behavior in interacting contexts. Although they are broadly used and studied, little is known about how their terms of service and posting rules affect the way users interact and information spreads. Acknowledging the relation between network connectivity and functionality, we compare the robustness of two different online social platforms, Twitter and Gab, with respect to dismantling strategies based on the recursive censor of users characterized by social prominence (degree) or intensity of inflammatory content (sentiment). We find that the moderated (Twitter) vs unmoderated (Gab) character of the network is not a discriminating factor for intervention effectiveness. We find, however, that more complex strategies based upon the combination of topological and content features may be effective for network dismantling. Our results provide useful indications to design better strategies for countervailing the production and dissemination of anti-social content in online social platforms

Sentiment Polarization and Balance among Users in Online Social Networks

Communication within online social network applications enables users to express and share sentiments electronically. Existing studies examined the existence or distribution of sentiments in online communication at a general level or in small-observed groups. Our paper extends this research by analyzing sentiment exchange within social networks from an ego-network perspective. We draw from research on social influence and social attachment to develop theories of node polarization, balance effects and sentiment mirroring within communication dyads. Our empirical analysis covers a multitude of social networks in which the sentiment valence of all messages was determined. Subsequently we studied ego-networks of focal actors (ego) and their immediate contacts. Results support our theories and indicate that actors develop polarized sentiments towards individual peers but keep sentiment in balance on the ego-network level. Further, pairs of nodes tend to establish similar attitudes towards each other leading to stable and polarized positive or negative relationships

Political conversations on Twitter in a disruptive scenario: The role of "party evangelists" during the 2015 Spanish general elections

"This is an Accepted Manuscript of an article published by Taylor & Francis in The Communication Review on 2019, available online: https://www.tandfonline.com/doi/full/10.1080/10714421.2019.1599642"[EN] During election campaigns, candidates, parties, and media share their relevance on Twitter with a group of especially active users, aligned with a particular party. This paper introduces the profile of ¿party evangelists,¿ and explores the activity and effects these users had on the general political conversation during the 2015 Spanish general election. On that occasion, the electoral expectations were uncertain for the two major parties (PP and PSOE) because of the rise of two emerging parties that were disrupting the political status quo (Podemos and Ciudadanos). This was an ideal situation to assess the differences between the evangelists of established and emerging parties. The paper evaluates two aspects of the political conversation based on a corpus of 8.9 million tweets: the retweet- ing effectiveness, and the sentiment analysis of the overall conver- sation. We found that one of the emerging party¿s evangelists dominated message dissemination to a much greater extent.The present research was supported by the Ministerio de Economia y Competitividad [CSO2013-43960-R] [CSO2016-77331-C2-1-R]. The present research was supported by the Ministerio de Economia y Competitividad, Spain, under Grants CSO2013-43960-R ("2015-2016 Spanish political parties' online campaign strategies") and CSO2016-77331-C2-1-R ("Strategies, agendas and discourse in electoral cybercampaigns: media and citizens"). This work was possible thanks to help received from Emilio Giner in his task of extracting the corpus of tweets and from assistance provided by Mike Thelwall and David Vilares in the use of the SentiStrength application. We have benefited from valuable comments on drafts of this article from professors Joaquín Aldás, Amparo Baviera-Puig, Guillermo López-García, and especially Lidia Valera-Ordaz.Baviera, T.; Sampietro, A.; García-Ull, FJ. (2019). Political conversations on Twitter in a disruptive scenario: The role of "party evangelists" during the 2015 Spanish general elections. The Communication Review. 22(2):117-138. https://doi.org/10.1080/10714421.2019.1599642S117138222Alvarez, R., Garcia, D., Moreno, Y., & Schweitzer, F. (2015). Sentiment cascades in the 15M movement. EPJ Data Science, 4(1). doi:10.1140/epjds/s13688-015-0042-4Anduiza, E., Cristancho, C., & Sabucedo, J. M. (2013). Mobilization through online social networks: the political protest of theindignadosin Spain. Information, Communication & Society, 17(6), 750-764. doi:10.1080/1369118x.2013.808360Anstead, N., & O’Loughlin, B. (2011). The Emerging Viewertariat and BBC Question Time. The International Journal of Press/Politics, 16(4), 440-462. doi:10.1177/1940161211415519Barabási, A.-L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509-512. doi:10.1126/science.286.5439.509Barberá, P. (2015). Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data. Political Analysis, 23(1), 76-91. doi:10.1093/pan/mpu011Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting From Left to Right. Psychological Science, 26(10), 1531-1542. doi:10.1177/0956797615594620Barberá, P., & Rivero, G. (2014). Understanding the Political Representativeness of Twitter Users. Social Science Computer Review, 33(6), 712-729. doi:10.1177/0894439314558836Berger, J., & Milkman, K. L. (2012). What Makes Online Content Viral? Journal of Marketing Research, 49(2), 192-205. doi:10.1509/jmr.10.0353Bigonha, C., Cardoso, T. N. C., Moro, M. M., Gonçalves, M. A., & Almeida, V. A. F. (2011). Sentiment-based influence detection on Twitter. Journal of the Brazilian Computer Society, 18(3), 169-183. doi:10.1007/s13173-011-0051-5Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. doi:10.1088/1742-5468/2008/10/p10008Bravo-Marquez, F., Mendoza, M., & Poblete, B. (2014). Meta-level sentiment models for big social data analysis. Knowledge-Based Systems, 69, 86-99. doi:10.1016/j.knosys.2014.05.016Casero-Ripollés, A., Feenstra, R. A., & Tormey, S. (2016). Old and New Media Logics in an Electoral Campaign. The International Journal of Press/Politics, 21(3), 378-397. doi:10.1177/1940161216645340Ceron, A., Curini, L., Iacus, S. M., & Porro, G. (2013). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 16(2), 340-358. doi:10.1177/1461444813480466Meeyoung Cha, Benevenuto, F., Haddadi, H., & Gummadi, K. (2012). The World of Connections and Information Flow in Twitter. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 42(4), 991-998. doi:10.1109/tsmca.2012.2183359Chadwick, A. (2013). The Hybrid Media System. doi:10.1093/acprof:oso/9780199759477.001.0001Cogburn, D. L., & Espinoza-Vasquez, F. K. (2011). From Networked Nominee to Networked Nation: Examining the Impact of Web 2.0 and Social Media on Political Participation and Civic Engagement in the 2008 Obama Campaign. Journal of Political Marketing, 10(1-2), 189-213. doi:10.1080/15377857.2011.540224(2014). Journal of Communication, 64(2). doi:10.1111/jcom.2014.64.issue-2Conover, M. D., Gonçalves, B., Flammini, A., & Menczer, F. (2012). Partisan asymmetries in online political activity. EPJ Data Science, 1(1). doi:10.1140/epjds6Coviello, L., Sohn, Y., Kramer, A. D. I., Marlow, C., Franceschetti, M., Christakis, N. A., & Fowler, J. H. (2014). Detecting Emotional Contagion in Massive Social Networks. PLoS ONE, 9(3), e90315. doi:10.1371/journal.pone.0090315D’heer, E., & Verdegem, P. (2014). Conversations about the elections on Twitter: Towards a structural understanding of Twitter’s relation with the political and the media field. European Journal of Communication, 29(6), 720-734. doi:10.1177/0267323114544866Dang-Xuan, L., Stieglitz, S., Wladarsch, J., & Neuberger, C. (2013). AN INVESTIGATION OF INFLUENTIALS AND THE ROLE OF SENTIMENT IN POLITICAL COMMUNICATION ON TWITTER DURING ELECTION PERIODS. Information, Communication & Society, 16(5), 795-825. doi:10.1080/1369118x.2013.783608Díaz-Parra, I., & Jover-Báez, J. (2016). Social movements in crisis? From the 15-M movement to the electoral shift in Spain. International Journal of Sociology and Social Policy, 36(9/10), 680-694. doi:10.1108/ijssp-09-2015-0101Dubois, E., & Gaffney, D. (2014). The Multiple Facets of Influence. American Behavioral Scientist, 58(10), 1260-1277. doi:10.1177/0002764214527088Enli, G. (2017). Twitter as arena for the authentic outsider: exploring the social media campaigns of Trump and Clinton in the 2016 US presidential election. European Journal of Communication, 32(1), 50-61. doi:10.1177/0267323116682802Felt, M. (2016). Social media and the social sciences: How researchers employ Big Data analytics. Big Data & Society, 3(1), 205395171664582. doi:10.1177/2053951716645828Ferrara, E., & Yang, Z. (2015). Measuring Emotional Contagion in Social Media. PLOS ONE, 10(11), e0142390. doi:10.1371/journal.pone.0142390(2015). Journal of Communication, 65(5). doi:10.1111/jcom.2015.65.issue-5Guerrero-Solé, F. (2018). Interactive Behavior in Political Discussions on Twitter: Politicians, Media, and Citizens’ Patterns of Interaction in the 2015 and 2016 Electoral Campaigns in Spain. Social Media + Society, 4(4), 205630511880877. doi:10.1177/2056305118808776Guo, L., & Vargo, C. (2015). The Power of Message Networks: A Big-Data Analysis of the Network Agenda Setting Model and Issue Ownership. Mass Communication and Society, 18(5), 557-576. doi:10.1080/15205436.2015.1045300Himelboim, I., McCreery, S., & Smith, M. (2013). Birds of a Feather Tweet Together: Integrating Network and Content Analyses to Examine Cross-Ideology Exposure on Twitter. Journal of Computer-Mediated Communication, 18(2), 40-60. doi:10.1111/jcc4.12001Huckfeldt, R., Johnson, P. E., & Sprague, J. (2004). Political Disagreement. doi:10.1017/cbo9780511617102Brundidge, J. (2010). Encountering «Difference» in the Contemporary Public Sphere: The Contribution of the Internet to the Heterogeneity of Political Discussion Networks. Journal of Communication, 60(4), 680-700. doi:10.1111/j.1460-2466.2010.01509.xJungherr, A. (2015). Analyzing Political Communication with Digital Trace Data. Contributions to Political Science. doi:10.1007/978-3-319-20319-5Jungherr, A., Jürgens, P., & Schoen, H. (2011). Why the Pirate Party Won the German Election of 2009 or The Trouble With Predictions: A Response to Tumasjan, A., Sprenger, T. O., Sander, P. G., & Welpe, I. M. «Predicting Elections With Twitter: What 140 Characters Reveal About Political Sentiment». Social Science Computer Review, 30(2), 229-234. doi:10.1177/0894439311404119Kaiser, H. F. (1960). The Application of Electronic Computers to Factor Analysis. Educational and Psychological Measurement, 20(1), 141-151. doi:10.1177/001316446002000116Klinger, U., & Svensson, J. (2014). The emergence of network media logic in political communication: A theoretical approach. New Media & Society, 17(8), 1241-1257. doi:10.1177/1461444814522952Lavezzolo, S., & Ramiro, L. (2017). Stealth democracy and the support for new and challenger parties. European Political Science Review, 10(2), 267-289. doi:10.1017/s1755773917000108McGregor, S. C., Mourão, R. R., & Molyneux, L. (2017). Twitter as a tool for and object of political and electoral activity: Considering electoral context and variance among actors. Journal of Information Technology & Politics, 14(2), 154-167. doi:10.1080/19331681.2017.1308289McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 27(1), 415-444. doi:10.1146/annurev.soc.27.1.415Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113. doi:10.1016/j.asej.2014.04.011Min, Y. (2004). News Coverage of Negative Political Campaigns. Harvard International Journal of Press/Politics, 9(4), 95-111. doi:10.1177/1081180x04271861Newman, M. (2010). Networks. doi:10.1093/acprof:oso/9780199206650.001.0001Orriols, L., & Cordero, G. (2016). The Breakdown of the Spanish Two-Party System: The Upsurge of Podemos and Ciudadanos in the 2015 General Election. South European Society and Politics, 21(4), 469-492. doi:10.1080/13608746.2016.1198454Park, C. S. (2013). Does Twitter motivate involvement in politics? Tweeting, opinion leadership, and political engagement. Computers in Human Behavior, 29(4), 1641-1648. doi:10.1016/j.chb.2013.01.044Riquelme, F., & González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Information Processing & Management, 52(5), 949-975. doi:10.1016/j.ipm.2016.04.003Robinson, J. P. (1976). Interpersonal Influence in Election Campaigns: Two Step-Flow Hypotheses. Public Opinion Quarterly, 40(3), 304. doi:10.1086/268307Robles, J. M., Díez, R., R. Castromil, A., Rodríguez, A., & Cruz, M. (2015). El movimiento 15-M en los medios y en las redes. Un análisis de sus estrategias comunicativas. Empiria. Revista de metodología de ciencias sociales, 0(32), 37. doi:10.5944/empiria.32.2015.15308Recerca. Revista de pensament i anàlisi. (s. f.). doi:10.6035/recercaSunstein, C. R. (2017). #Republic. doi:10.1515/9781400884711Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558. doi:10.1002/asi.21416Vaccari, C., Chadwick, A., & O’Loughlin, B. (2015). Dual Screening the Political: Media Events, Social Media, and Citizen Engagement. Journal of Communication, 65(6), 1041-1061. doi:10.1111/jcom.12187Vergeer, M., & Hermans, L. (2013). Campaigning on Twitter: Microblogging and Online Social Networking as Campaign Tools in the 2010 General Elections in the Netherlands. Journal of Computer-Mediated Communication, 18(4), 399-419. doi:10.1111/jcc4.12023Vilares, D., Thelwall, M., & Alonso, M. A. (2015). The megaphone of the people? Spanish SentiStrength for real-time analysis of political tweets. Journal of Information Science, 41(6), 799-813. doi:10.1177/0165551515598926Weimann, G. (1991). The Influentials: Back to the Concept of Opinion Leaders? Public Opinion Quarterly, 55(2), 267. doi:10.1086/269257Wu, S., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011). Who says what to whom on twitter. Proceedings of the 20th international conference on World wide web - WWW ’11. doi:10.1145/1963405.1963504Xu, W. W., Sang, Y., Blasiola, S., & Park, H. W. (2014). Predicting Opinion Leaders in Twitter Activism Networks. American Behavioral Scientist, 58(10), 1278-1293. doi:10.1177/0002764214527091Zollo, F., Novak, P. K., Del Vicario, M., Bessi, A., Mozetič, I., Scala, A., … Quattrociocchi, W. (2015). Emotional Dynamics in the Age of Misinformation. PLOS ONE, 10(9), e0138740. doi:10.1371/journal.pone.013874

Public survey instruments for business administration using social network analysis and big data

Purpose: The subject matter of this research is closely intertwined with the scientific discussion about the necessity of developing and implementing practice-oriented means of measuring social well-being taking into account the intensity of contacts between individuals. The aim of the research is to test the toolkit for analyzing social networks and to develop a research algorithm to identify sources of consolidation of public opinion and key agents of influence. The research methodology is based on postulates of sociology, graph theory, social network analysis and cluster analysis. Design/Methodology/Approach: The basis for the empirical research was provided by the data representing the reflection of social media users on the existing image of Russia and its activities in the Arctic, chosen as a model case. Findings: The algorithm allows to estimate the density and intensity of connections between actors, to trace the main channels of formation of public opinion and key agents of influence, to identify implicit patterns and trends, to relate information flows and events with current information causes and news stories for the subsequent formation of a "cleansed" image of the object under study and the key actors with whom this object is associated. Practical Implications: The work contributes to filling the existing gap in the scientific literature, caused by insufficient elaboration of the issues of applying the social network analysis to solve sociological problems. Originality/Value: The work contributes to filling the existing gap in the scientific literature formed as a result of insufficient development of practical issues of using analysis of social networks to solve sociological problems.peer-reviewe

Analyzing digital societal interactions and sentiment classification in Twitter (X) during critical events in Chile

This study explores the influence of social media content on societal attitudes and actions during critical events, with a special focus on occurrences in Chile, such as the COVID-19 pandemic, the 2019 protests, and the wildfires in 2017 and 2023. By leveraging a novel tweet dataset, this study introduces new metrics for assessing sentiment, inclusivity, engagement, and impact, thereby providing a comprehensive framework for analyzing social media dynamics. The methodology employed enhances sentiment classification through the use of a Deep Random Vector Functional Link (D-RVFL) neural network, which demonstrates superior performance over traditional models such as Support Vector Machines (SVM), naive Bayes, and back propagation (BP) neural networks, achieving an overall average accuracy of 78.30% (0.17). This advancement is attributed to deep learning techniques with direct input–output connections that facilitate faster and more precise sentiment classification. This analysis differentiates the roles of influencers, press radio, and television handlers during crises, revealing how various social media actors affect information dissemination and audience engagement. By dissecting online behaviors and classifying sentiments using the RVFL network, this study sheds light on the effects of the digital landscape on societal attitudes and actions during emergencies. These findings underscore the importance of understanding the nuances of social media engagement to develop more effective crisis communication strategies