Quantifying Controversy in Social Media
Open accessAbstract
Which topics spark the most heated debates in social media? Identifying these topics is a first step towards creating systems which pierce echo chambers. In this paper, we perform a systematic methodological study of controversy detection using social media network structure and content.
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Cites
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“Inpsired by the Davies-Bouldin (DB) index [9], we define the Embedding Controversy measure EC as”
MethodAn Information Flow Model for Conflict and Fission in Small Groups
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“However, how well does it generalize to datasets from different domains? We obtain a comprehensive group of datasets kindly shared by authors of previous works: Political blogs, links between blogs discussing politics in the US [1]; Twitter politics, Twitter messages pertaining to the 2010 midterm election in US [8];…”
BackgroundStructural Holes: The Social Structure of CompetitionPreprint
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MethodPerformance evaluation of some clustering algorithms and validity indices
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MethodThe Consequences of Cross-Cutting Networks for Political Participation
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“controversy on social media [21, 22] by connecting social media users with content outside their own bubbles. Finally, polarization by itself may not be a wholly negative phenomenon. Several studies [11, 42] argue that a democracy needs deliberation, and polarization enable such a deliberation to happen in the public,…”
BackgroundPolitical Polarization on Twitter
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“We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy, and show that content features are vastly less helpful in this task.”
BackgroundA Measure of Polarization on Social Media Networks Based on Community Boundaries
Guerra, P., Meira, W., Cardie, C., & Kleinberg, R. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14421 · Cited by 188 · cited 2× · see citing works
“We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy, and show that content features are vastly less helpful in this task.”
BackgroundInfluentialEncouraging Reading of Diverse Political Viewpoints with a Browser Widget
Munson, S. A., Lee, S., & Resnick, P. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14429 · Cited by 136 · see citing works
Managing political differences in social media
Grevet, C., Terveen, L., & Gilbert, É. (2014) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2531602.2531676 · Cited by 79 · cited 1× · see citing works
“For example, “#beefban” is a special keyword (a “hashtag”) that was employed by Twitter users to signal that their posts referred to a decision of the Indian government, in March 2015, about the consumption of beef meat in India.”
BackgroundQuantifying Political Polarity Based on Bipartite Opinion Networks
Akoglu, L. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14524 · Cited by 79 · see citing works
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“For example, “#beefban” is a special keyword (a “hashtag”) that was employed by Twitter users to signal that their posts referred to a decision of the Indian government, in March 2015, about the consumption of beef meat in India.”
BackgroundControversy and Sentiment in Online NewsPreprint
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Identifying Controversial Issues and Their Sub-topics in News Articles
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BackgroundSocial Determinants of Content Selection in the Age of (Mis)Information
Bessi, A., Caldarelli, G., Vicario, M. D., Antonio Scala, A., & Quattrociocchi, W. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13734-6_18 · Cited by 45 · cited 1× · see citing works
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BackgroundCharacterizing Information Diets of Social Media Users
Kulshrestha, J., Zafar, M. B., Espín-Noboa, L., Gummadi, K. P., & Ghosh, S. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v9i1.14634 · Cited by 44 · cited 2× · see citing works
“Unlike previous work, rather than identifying controversy in a single hand-picked topic and use domain-specific knowledge, we focus on comparing topics in any domain.”
BackgroundData Portraits: Connecting People of Opposing ViewsPreprint
Graells-Garrido, E., Lalmas, M., & Quercia, D. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1311.4658 · Cited by 31 · see citing works
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A Balanced News Diet, Not Selective Exposure: Evidence from a Direct Measure of Media ExposurePreprint
LaCour, M. J. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2050762 · Cited by 9 · see citing works
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The echo chamber effect on social media
Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2023301118 · Cited by 2,092 · see citing works
Quantifying controversy on social media
Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M. (2018) · ACM Transactions on Social Computing · doi:10.1145/3140565 · Cited by 348 · see citing works
“We use this approach in an earlier version of this work [20], with samples of 10,000 random walks.”
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Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. (2018) · Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18 · doi:10.1145/3178876.3186139 · Cited by 335 · see citing works
“For example, in the context of Twitter, clusters in retweet networks have been found to correlate with the political ideologies of Twitter users [7, 10, 15].”
BackgroundSelf-citationCommunity Interaction and Conflict on the Web
Kumar, S., Hamilton, W. L., Leskovec, J., & Jurafsky, D. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186141 · Cited by 333 · see citing works
“forms has focused primarily on controversies [3, 13, 20, 21, 45, 52] and anti-social behavior, in the form of trolling [11, 12, 42], sockpuppetry [39], harassment and cyberbullying [8, 23, 32, 35, 55, 66, 79], vandalism [43], hate speech [9, 15, 51, 56, 72], and others [17, 24, 40, 41, 44, 46, 54, 68, 78, 81].”
BackgroundPolarization and Fake News: Early Warning of Potential Misinformation Targets
Vicario, M. D., Quattrociocchi, W., Scala, A., & Zollo, F. (2019) · ACM Transactions on the Web · doi:10.1145/3316809 · Cited by 280 · see citing works
“A large body of work targeted controversy in the political domain [21, 22], and studied controversy detection using social media network structure and content [23].”
BackgroundSemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles
Martino, G. D. S., Barrón‐Cedeño, A., Wachsmuth, H., Petrov, R., & Nakov, P. (2020) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2020.semeval-1.186 · Cited by 202 · see citing works
“However, most previous work has performed analysis at the document level only (Rashkin et al., 2017; Barr´on-Cede˜no et al., 2019a) or has analyzed the general patterns of online propaganda (Garimella et al., 2015; Chatfield et al., 2015).”
MethodA Long-Term Analysis of Polarization on Twitter
Garimella, V. R. K., & Weber, I. G. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14918 · Cited by 189 · see citing works
“On a similar note, [6] show that most polarized discussions on social media have a welldefined structure, when looking at the retweet network.”
BackgroundDeep learning for Arabic NLP: A survey
Al‐Ayyoub, M., Nuseir, A., Alsmearat, K., Jararweh, Y., & Gupta, B. B. (2017) · Journal of Computational Science · doi:10.1016/j.jocs.2017.11.011 · Cited by 173 · see citing works
Measuring and moderating opinion polarization in social networks
Matakos, A., Terzi, E., & Tsaparas, P. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-017-0527-9 · Cited by 164 · see citing works
“…a lot of work on measures for quantifying the polarization observed in online social networks and social media (Akoglu 2014; Conover et al. 2011; Garimella et al. 2016; Guerra et al. 2013; Amelkin et al. 2015; Dandekar et al. 2013) and model its emergence (Dandekar et al. 2013; Vicario et al.…”
BackgroundReducing Controversy by Connecting Opposing Views
Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. (2017) · Proceedings of the Tenth ACM International Conference on Web Search and Data Mining · doi:10.1145/3018661.3018703 · Cited by 165 · see citing works
“As reviewed in Section 2, there are several measures for quantifying controversy in social media [2, 3, 7, 11, 25, 26].”
BackgroundMethodInfluentialSelf-citationMinimizing Polarization and Disagreement in Social Networks
Musco, C., Musco, C., & Tsourakakis, C. E. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186103 · Cited by 132 · see citing works
“Close to our work at a conceptual level also lies the work of Garimella, Morales, Gionis, and Mathioudakis [25], which focuses on Twitter data.”
BackgroundFalling into the Echo Chamber: The Italian Vaccination Debate on Twitter
Cossard, A., De Francisci Morales, G., Kalimeri, K., Mejova, Y., Paolotti, D., & Starnini, M. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7285 · Cited by 126 · see citing works
“We use the Random Walk Controversy (RWC) score introduced by Garimella et al. (2016).”
BackgroundMethodInfluentialHuman migration: the big data perspective
Sîrbu, A., Andrienko, G., Andrienko, N. V., Boldrini, C., Conti, M., Giannotti, F., Guidotti, R., Bertoli, S., Kim, J., Muntean, C. I., et al. (2020) · International Journal of Data Science and Analytics · doi:10.1007/s41060-020-00213-5 · Cited by 106 · see citing works
“controversy and topic tracking in time have been proposed [46,69].”
BackgroundMethodSearch bias quantification: investigating political bias in social media and web search
Kulshrestha, J., Eslami, M., Messias, J., Zafar, M. B., Ghosh, S., Gummadi, K. P., & Karahalios, K. G. (2018) · Information Retrieval · doi:10.1007/s10791-018-9341-2 · Cited by 103 · see citing works
“Considerable research effort has also been dedicated to studying controversies and controversial topics online (Coletto et al. 2017; Garimella et al. 2016; Lu et al. 2015).”
BackgroundNo echo in the chambers of political interactions on Reddit
De Francisci Morales, G., Monti, C., & Starnini, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-81531-x · Cited by 98 · see citing works
“(2) and (5), respectively, is reported in Fig.”
BackgroundGaumont, N., Panahi, M., & Chavalarias, D. (2018) · PLOS ONE · doi:10.1371/journal.pone.0201879 · Cited by 88 · see citing works
Interian, R., G. Marzo, R., Mendoza, I., & Ribeiro, C. C. (2023) · International Transactions in Operational Research · doi:10.1111/itor.13224 · Cited by 67 · see citing works
“They observed that, as the conflict intensifies, the RWC and the standard deviation of the overall sentiment expressed by the opposing groups are positively correlated and increase in unison.)”
BackgroundMethodInfluentialOn the nature of real and perceived bias in the mainstream media
Elejalde, E., Ferres, L., & Herder, E. (2018) · PLOS ONE · doi:10.1371/journal.pone.0193765 · Cited by 64 · see citing works
“In this sense, we could benefit from advances in systems that focus on identifying controversial topics in social media [54].”
BackgroundPolarization and Fake News: Early Warning of Potential Misinformation TargetsPreprint
Vicario, M. D., Quattrociocchi, W., Antonio Scala, A., & Zollo, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.01400 · Cited by 60 · see citing works
“A large body of work targeted controversy in the political domain [21, 22], and studied controversy detection using social media network structure and content [23].”
BackgroundQuantifying and Minimizing Risk of Conflict in Social Networks
Chen, X., Lijffijt, J., & Bie, T. D. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220074 · Cited by 59 · see citing works
Echo Chambers on Social Media: A comparative analysisPreprint
Cinelli, M., Morales, G. D. F., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.09603 · Cited by 58 · see citing works
“The presence of echo chambers can be gauged by simple models of information spreading: users are expected to exchange information more likely with peers sharing a similar leaning [21, 44, 51].”
BackgroundThe Effect of Collective Attention on Controversial Debates on Social Media
Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. (2017) · Proceedings of the 2017 ACM on Web Science Conference · doi:10.1145/3091478.3091486 · Cited by 56 · see citing works
Self-citationSocial Media Is Polarized, Social Media Is Polarized
Nelimarkka, M., Laaksonen, S.-M., & Semaan, B. (2018) · Designing Interactive Systems Conference · doi:10.1145/3196709.3196764 · Cited by 51 · see citing works
Exploiting Open Data to analyze discussion and controversy in online citizen participation
Iván Cantador, A., Cortés-Cediel, M. E., & Fernández, M. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102301 · Cited by 50 · see citing works
Automatic controversy detection in social media: A content-independent motif-based approach
Coletto, M., Garimella, K., Gionis, A., & Lucchese, C. (2017) · Online Social Networks and Media · doi:10.1016/j.osnem.2017.10.001 · Cited by 49 · see citing works
Self-citationTrouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop
Chang, J. P., & Danescu-Niculescu-Mizil, C. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1481 · Cited by 47 · see citing works
“, 2013) and graph representations of conversations (Garimella et al., 2017; Zhang et al., 2018b).”
BackgroundCharacterizing Online Public Discussions through Patterns of Participant Interactions
Zhang, J., Danescu-Niculescu-Mizil, C., Sauper, C., & Taylor, S. J. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274467 · Cited by 47 · see citing works
“…largely considered the social structure of entire communities induced over the course of many different discussions [12, 43, 44, 46], applying the models formulated to tasks such as detecting factions [1, 24], identifying influential individuals [40, 51] or investigating social tie breaks [35].”
BackgroundJoint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter
Lahoti, P., Garimella, K., & Gionis, A. (2018) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3159652.3159669 · Cited by 43 · see citing works
Self-citationTell me Something My Friends do not Know: Diversity Maximization in Social Networks
Matakos, A., Tu, S., & Gionis, A. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-020-01456-1 · Cited by 41 · see citing works
“Proposed approaches include detecting [2], [4], [5], [10], [12], [16] and reducing polarization of opinions [22], [24], recommending users-to-follow and content to bridge opposing views [11], [19], [23], and balancing information exposure in a social network [9].”
BackgroundSelf-citationPredicting News Popularity by Mining Online Discussions
Rizos, G., Papadopoulos, S., & Kompatsiaris, I. Y. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872518.2890096 · Cited by 36 · see citing works
“We intend to further add information modalities, such as the connectivity of underlying user friendship, subscription or 4 https://www.reddit.com/2qtfdu/ reply graphs in order to capture common user preferences when predicting news story popularity or controversiality as in [4].”
BackgroundNews Consumption during the Italian Referendum: A Cross-Platform Analysis on Facebook and Twitter
Vicario, M. D., Gaito, S., Quattrociocchi, W., Zignani, M., & Zollo, F. (2017) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2017.33 · Cited by 36 · see citing works
Reducing Controversy by Connecting Opposing Views
Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/731 · Cited by 39 · see citing works
Self-citationBurst Your Bubble! An Intelligent System for Improving Awareness of Diverse Social Opinions
Gao, M., Do, H. J., & Fu, W. (2018) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3172944.3172970 · Cited by 35 · see citing works
“In addition, Garimella and his colleagues [27] attempted to bridge echo chambers using algorithmic techniques with endorsement graph and random-walk controversy score [26].”
Background(Re)Design to Mitigate Political Polarization
Nelimarkka, M., Rancy, J.-P., Grygiel, J., & Semaan, B. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359243 · Cited by 36 · see citing works
Users Are Known by the Company They Keep
Thonet, T., Cabanac, G., Boughanem, M., & Pinel-Sauvagnat, K. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132897 · Cited by 32 · see citing works
Guerra, P., Nalon, R., Assunção, R., & Meira, W. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14971 · Cited by 32 · see citing works
“In general, studies of polarized online communities induced by topics such as Politics and public policies do not conduct any explicit analysis of antagonism at the edge granularity, and the degree of separation between communities as well as the controversial nature of the topic is accepted as sufficient evidence of…”
BackgroundLanguage in Our Time: An Empirical Analysis of Hashtags
Zhang, Y. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313480 · Cited by 31 · see citing works
“Researchers have studied hashtags from many perspectives [2, 5, 6, 8–10, 12, 14–16, 23, 24, 26–28, 30, 33, 36, 37, 41– 43, 49, 52, 54, 55, 58, 60, 63, 65].”
Maximizing the Diversity of Exposure in a Social Network
Matakos, A., Aslay, Ç., Galbrun, E., & Gionis, A. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3038711 · Cited by 28 · see citing works
Self-citationThe impact of noise and topology on opinion dynamics in social networks
Stern, S., & Livan, G. (2021) · Royal Society Open Science · doi:10.1098/rsos.201943 · Cited by 27 · see citing works
Huang, Z., Silva, A., & Singh, A. K. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3498454 · Cited by 27 · see citing works
“For example, [15] measures polarization based on the concentration of high-degree nodes in the community boundaries, and [10] extends it with betweenness and inter-community random-walk transitions.”
BackgroundStudying the controversy in online crowds’ interactions
Al‐Ayyoub, M., Rabab’ah, A., Jararweh, Y., Al-Kabi, M. N., & Gupta, B. B. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.03.022 · Cited by 27 · see citing works
Perception of social phenomena through the multidimensional analysis of online social networks
Coletto, M., Esuli, A., Lucchese, C., Muntean, C. I., Nardini, F. M., Perego, R., & Renso, C. (2017) · Online Social Networks and Media · doi:10.1016/j.osnem.2017.03.001 · Cited by 27 · see citing works
Automatically Identifying Good Conversations Online (Yes, They Do Exist!)
Napoles, C., Pappu, A., & Tetreault, J. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14959 · Cited by 26 · see citing works
Maximizing the Diversity of Exposure in a Social Network
Aslay, Ç., Matakos, A., Galbrun, E., & Gionis, A. (2018) · Proceedings · doi:10.1109/icdm.2018.00102 · Cited by 26 · see citing works
Self-citationColetto, M., Esuli, A., Lucchese, C., Muntean, C. I., Nardini, F. M., Perego, R., & Renso, C. (2016) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2016.7752401 · Cited by 26 · see citing works
“Recently new approaches based on polarization, controversy and topic tracking in time have been proposed [13], [14].”
BackgroundMethodMining Cross-Cultural Differences and Similarities in Social Media
Lin, B. Y., Xu, F. F., Zhu, K. Q., & Hwang, S.-W. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1066 · Cited by 25 · see citing works
“Both Elahi and Monachesi (2012) and Garimella et al. (2016a) find such social words are most effective culture/socio-linguistic features in identifying cross-cultural differences.”
BackgroundMethodInfluentialPredicting the Role of Political Trolls in Social Media
Atanasov, A., Morales, G. D. F., & Nakov, P. (2019) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k19-1096 · Cited by 23 · see citing works
“Del Vicario et al. (2016) have also suggested that the spreading of misinformation on-line is fostered by the presence of polarization and echo chambers in social media (Garimella et al., 2016, 2017, 2018).”
BackgroundMendoza, M., Parra, D., & Soto, Á. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102366 · Cited by 25 · see citing works
Explaining Controversy on Social Media via Stance Summarization
Jang, M., & Allan, J. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3210143 · Cited by 24 · see citing works
Evidence of Demographic rather than Ideological Segregation in News Discussion on Reddit
Monti, C., D'Ignazi, J., Starnini, M., & De Francisci Morales, G. (2023) · Proceedings of the ACM Web Conference 2023 · doi:10.1145/3543507.3583468 · Cited by 26 · see citing works
“Since then, their result has been replicated several times, on different topics, and in various contexts [17, 19, 26, 28, 56].”
BackgroundLenti, J., Mejova, Y., Kalimeri, K., Panisson, A., Paolotti, D., Tizzani, M., & Starnini, M. (2023) · JMIR Infodemiology · doi:10.2196/44714 · Cited by 23 · see citing works
“Along the same research line, the Random Walk Controversy (RWC) score [38] quantifies how controversial the topics discussed over a certain social network are as the probability of an average user being exposed to information from their own side versus from the opposing side.”
BackgroundA Motif-Based Approach for Identifying Controversy
Coletto, M., Garimella, K., Gionis, A., & Lucchese, C. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14949 · Cited by 23 · see citing works
“(Garimella et al. 2016), who also aim at identifying controversies based on the analysis of the network structure.”
BackgroundResultSelf-citationZapata, G., Murga, J., Raymundo, C., Domínguez, F., Moguerza, J. M., & Álvarez-Rodríguez, J. M. (2019) · Journal of Intelligent Information Systems · doi:10.1007/s10844-019-00564-x · Cited by 22 · see citing works
Telling Apart Tweets Associated with Controversial versus Non-Controversial Topics
Addawood, A., Rezapour, R., Abdar, O., & Diesner, J. (2017) · Workshop on Natural Language Processing and Computational Social Science (NLP+CSS) · doi:10.18653/v1/w17-2905 · Cited by 21 · see citing works
“As with many other outlets of public opinion, Twitter features the emergence of polarization around controversial issues (Addawood & Bashir, 2016; Garimella, De Francisci Morales, Gionis, & Mathioudakis, 2016), and provides a forum where people can express their opinions, which may be conflicting…”
BackgroundMethodImpact of Content Features for Automatic Online Abuse Detection
Papégnies, É., Labatut, V., Dufour, R., & Linarès, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-77116-8_30 · Cited by 20 · see citing works
“When quantifying controversy in social media [12], the structure of the community network is exploited to identify topics that are likely to trigger strong disagreements between users.”
BackgroundSeparating Polarization from Noise: Comparison and Normalization of Structural Polarization Measures
Salloum, A., Chen, T. H. Y., & Kivelä, M. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512962 · Cited by 20 · see citing works
“Exceptions to this include a small number of studies that compare the performance of some scores in classifying polarized political systems [23, 32] or those that show certain scores are invariant to network size [64].”
BackgroundMethodInfluentialAgent-Based Modelling Meets Generative AI in Social Network Simulations
Ferraro, A., Galli, A., Gatta, V. L., Postiglione, M., Orlando, G. M., Russo, D., Riccio, G., Romano, A., & Moscato, V. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-78541-2_10 · Cited by 19 · see citing works
Fine-grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning
Fagni, T., & Cresci, S. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13112 · Cited by 17 · see citing works
“Existing approaches to the prediction of political leaning mainly focus on analyzing only the social or interaction networks (Garimella et al., 2016; Wong et al., 2016), or only the content of shared messages (Pla & Hurtado, 2014; Di Giovanni et al.”
BackgroundConversational Networks for Automatic Online Moderation
Papégnies, É., Labatut, V., Dufour, R., & Linarès, G. (2019) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2018.2887240 · Cited by 17 · see citing works
Viewpoint Discovery and Understanding in Social Networks
Quraishi, M. A. N., Fafalios, P., & Herder, E. (2018) · ACM Web Science Conference (WebSci) · doi:10.1145/3201064.3201076 · Cited by 17 · see citing works
“As regards social media, controversy detection has been studied on Twitter us-ing both supervised [35] and unsupervised [18] methods.”
BackgroundMethodInfluentialPolarized User and Topic Tracking in Twitter
Coletto, M., Lucchese, C., Orlando, S., & Perego, R. (2016) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2911451.2914716 · Cited by 16 · see citing works
“This notion is connected with the concept of controversy in Social M edia, which have been studied, mostly in political contexts, usin g data coming from different sources [1,4,6,8].”
BackgroundColetto, M., Esuli, A., Lucchese, C., Muntean, C. I., Nardini, F. M., Perego, R., & Renso, C. (2016) · arXiv (Cornell University) · doi:10.5555/3192424.3192657 · Cited by 16 · see citing works
The evolving perception of controversial movies
Amendola, L., Marra, V., & Quartin, M. (2015) · RePEc: Research Papers in Economics · Cited by 13 · see citing works
Authority without Care: Moral Values behind the Mask Mandate Response
Mejova, Y., Kalimeri, K., & Morales, G. D. F. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22173 · Cited by 13 · see citing works
Modeling Controversy within Populations
Jang, M., Dori-Hacohen, S., & Allan, J. (2017) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3121050.3121067 · Cited by 13 · see citing works
Badami, M., Nasraoui, O., Sun, W., & Shafto, P. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258231 · Cited by 13 · see citing works
PrCP: Pre-recommendation Counter-Polarization
Badami, M., Nasraoui, O., & Shafto, P. (2018) · International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management · doi:10.5220/0006938702820289 · Cited by 13 · see citing works
Proceedings of the Second Workshop on NLP and Computational Social Science
Hovy, D., Volkova, S., Bamman, D., Jurgens, D., O'connor, B., Tsur, O., & Doğruöz, A. S. (2017) · doi:10.18653/v1/w17-29 · Cited by 13 · see citing works
Quantifying polarization in online political discourse
Muñoz, P., Bellogín, A., Barba-Rojas, R., & Díez, F. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00480-3 · Cited by 19 · see citing works
Exploring Controversy in Twitter
Garimella, K., Mathioudakis, M., Morales, G. D. F., & Gionis, A. (2016) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2818052.2874318 · Cited by 12 · see citing works
“Our system is the first one to attempt controversy detection in the wild, on any topic, and without human data curation [4].”
BackgroundResultInfluentialSelf-citationTweeting PP: an analysis of the 2015–2016 Planned Parenthood controversy on Twitter
Han, L. L., Han, L. Y., Darney, B. G., & Rodriguez, M. I. (2017) · Contraception · doi:10.1016/j.contraception.2017.08.011 · Cited by 13 · see citing works
Measuring the controversy level of Arabic trending topics on Twitter
Rabab’ah, A., Al‐Ayyoub, M., Jararweh, Y., & Al-Kabi, M. N. (2016) · International Conference on Information and Communication Systems (ICICS) · doi:10.1109/iacs.2016.7476097 · Cited by 11 · see citing works
The Nature of Real and Perceived Bias in Chilean Media
Elejalde, E., Ferres, L., & Herder, E. (2017) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3078714.3078724 · Cited by 11 · see citing works
Combining Network and Language Indicators for Tracking Conflict Intensity
Rumshisky, A., Gronas, M., Potash, P., Dubov, M., Romanov, A., Kulshreshtha, S., & Gribov, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67256-4_31 · Cited by 11 · see citing works
Hostilité et prosélytisme des communautés politiques
Chavalarias, D., Gaumont, N., & Panahi, M. (2019) · Réseaux · doi:10.3917/res.214.0067 · Cited by 11 · see citing works
Socio-semantic networks as mutualistic networks
St-Onge, J., Renaud-Desjardins, L., Mongeau, P., & Saint-Charles, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-05743-5 · Cited by 10 · see citing works
“Echo chambers are commonly defined as subsets of individuals who are primarily exposed to ideologies that agree with their own political leaning 41 .”
BackgroundThe evolving perception of controversial movies
Amendola, L., Marra, V., & Quartin, M. (2015) · Humanities and Social Sciences Communications · doi:10.1057/palcomms.2015.38 · Cited by 10 · see citing works
“In Garimella et al. (2015) the authors focus on measuring the degree of controversy in Twitter conversations on particular topics, such as news items, with the aim of automatizing the task of finding and comparing controversial news.”
BackgroundAragón, P., Volkovich, Y., Laniado, D., & Kaltenbrunner, A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14723 · Cited by 10 · see citing works
“To exclude anecdotal interactions between users which might not be enough of a signal to infer endorsement (Garimella et al. 2015) and to highlight the structure of the expected clusters, we only keep the interactions between any pair of nodes that occurred at least 3 times: an edge from user A to…”
BackgroundPredictive Model based on Sentiment Analysis for Peruvian SMEs in the Sustainable Tourist Sector
Zapata, G., Murga, J., Raymundo, C., Álvarez-Rodríguez, J. M., & Domínguez, F. (2017) · International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management · doi:10.5220/0006583302320240 · Cited by 10 · see citing works
Kane, B., & Luo, J. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622535 · Cited by 10 · see citing works
“Finally, some studies have successfully avoided the need for external bias measures altogether by utilizing a conversation graph and creating a partition to represent sides of a debate (Garimella et al. 2017; Agrawal et al. 2003).”
BackgroundPolarized User and Topic Tracking in TwitterPreprint
Coletto, M., Lucchese, C., Orlando, S., & Perego, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.08686 · Cited by 10 · see citing works
“This notion is connected with the concept of controversy in Social M edia, which have been studied, mostly in political contexts, usin g data coming from different sources [1,4,6,8].”
BackgroundLearning Ideological Embeddings from Information Cascades
Monti, C., Manco, G., Aslay, Ç., & Bonchi, F. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482444 · Cited by 9 · see citing works
“the source regarding that specific topic [26].”
BackgroundInfluentialPredicting Contradiction Intensity
Badache, I., Fournier, S., & Chifu, A.-G. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3210098 · Cited by 9 · see citing works
“In the literature, the detection of controversies has been addressed both by supervised methods as in [2, 13, 22] or by unsupervised methods as in [4, 5, 10].”
BackgroundMethodA Motif-based Approach for Identifying Controversy
Coletto, M., Garimella, K., Gionis, A., & Lucchese, C. (2017) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.48550/arxiv.1703.05053 · Cited by 9 · see citing works
“(Garimella et al. 2016), who also aim at identifying controversies based on the analysis of the network structure.”
BackgroundResultSelf-citationPriharsari, D., & Abedin, B. (2021) · Information Technology and People · doi:10.1108/itp-10-2020-0707 · Cited by 9 · see citing works
Exploring controversy via sentiment divergences of aspects in reviews
Xu, J., Tao, Y., Lin, H. B., Zhu, R., & Yan, Y. (2017) · IEEE Pacific Visualization Symposium · doi:10.1109/pacificvis.2017.8031600 · Cited by 9 · see citing works
Discovering Topically- and Temporally-Coherent Events in Interaction Networks
Xiao, H., Rozenshtein, P., & Gionis, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46227-1_43 · Cited by 8 · see citing works
“India’s new law on banning on beef is a controversial topic [9].”
BackgroundSelf-citationModeling Political Activism around Gun Debate via Social Media
Mejova, Y., An, J., Morales, G. D. F., & Kwak, H. (2022) · ACM Transactions on Social Computing · doi:10.1145/3532102 · Cited by 8 · see citing works
“The intense debate has moved onto online—gun control has been considered as one of the most controversial issues presented social media and both views have been reinforced and propagated via social networks [46].”
BackgroundUs and Them: Adversarial Politics on Twitter
Guimarães, A., Wang, L., & Weikum, G. (2017) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2017.119 · Cited by 8 · see citing works
“A fundamental approach to measuring the amount of controversy in social media discussions is presented in [10] and further expanded in [11] to cover the evolution of”
BackgroundSentiment analysis of controversial topics on Pakistan's Twitter user-base
Zafar, S., Sarwar, U., Gilani, Z., & Qadir, J. (2016) · ACM Symposium on Computing for Development (ACM DEV) · doi:10.1145/3001913.3006644 · Cited by 8 · see citing works
Garimella, K., iMorales, G. D. F., Gionis, A., & Mathioudakis, M. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3054737 · Cited by 8 · see citing works
Self-citationBalancing Opposing Views to Reduce Controversy.Preprint
Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M. (2016) · arXiv (Cornell University) · Cited by 8 · see citing works
Self-citationReconstructing spatial information diffusion networks with heterogeneous agents and text contents
Ye, X., Wang, W., Zhang, X., Li, Z., Yu, D., Du, J., & Chen, Z. (2021) · Transactions in GIS · doi:10.1111/tgis.12747 · Cited by 7 · see citing works
“Given the conditional independence of the increment nx,i,t − nx,i,t− for different x, the law of large numbers guarantees that asymptotically replacing dr(x, i, tl)∕dt with nx� ,i,t − nx� ,i,t− in Equation (3) yields the equivalent approximation to dr(x, i, tl)∕dt, but unlike the known dr(x, i, tl)∕dt, values of nx� ,…”
BackgroundMethodInfluentialQuoting is not Citing: Disentangling Affiliation and Interaction on Twitter
Roth, C., St-Onge, J., & Herms, K. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-93409-5_58 · Cited by 7 · see citing works
“These observations depend nonetheless heavily on topics [4,10], and on link types: in particular, affiliation links generally configure networks where homophily is much stronger than with interaction links [23].”
BackgroundNet activism and whistleblowing on YouTube: a text mining analysis
Turenne, N. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13777-0 · Cited by 7 · see citing works
“The authors of [23] Multimedia Tools and Applications”
BackgroundFactors in Recommending Contrarian Content on Social Media
Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M. (2017) · ACM Web Science Conference (WebSci) · doi:10.1145/3091478.3091515 · Cited by 6 · see citing works
“We use a recently-proposed methodology to de ne the polarization score for each user in the graph [4].”
MethodSelf-citationTimmermans, B., Kuhn, T., Beelen, K., & Aroyo, L. M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67256-4_23 · Cited by 6 · see citing works
“This digital nature has naturally led to many computational approaches to capture and analyze controversy [3,20,40,6,15,30].”
BackgroundOnline network organization of Barcelona en Comú, an emergent movement-party
Aragón, P., Gallego, H., Laniado, D., Volkovich, Y., & Kaltenbrunner, A. (2017) · Computational Social Networks · doi:10.1186/s40649-017-0044-4 · Cited by 5 · see citing works
“To exclude anecdotal interactions between users which might not be enough of a signal to infer endorsement [25] and to highlight the structure of the expected clusters, the network only contains the interactions between any pair of nodes that occurred at least 3 times: an edge from user A to user B implies that user A…”
BackgroundKaplun, K., Leberknight, C. S., & Feldman, A. (2018) · Hellenic Conference on Artificial Intelligence · doi:10.1145/3200947.3201016 · Cited by 5 · see citing works
A Framework for the Unsupervised Modeling and Extraction of Polarization Knowledge from News Media
Paschalides, D., Pallis, G., & Dikaiakos, M. D. (2024) · ACM Transactions on Social Computing · doi:10.1145/3703594 · Cited by 6 · see citing works
“This reliance can prevent the identification of evolving group dynamics and obscure the complexity of polarization in different contexts [31, 63].”
Influential
- Authors
- Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M.
- Affiliations
- Aalto University (FI)
- Aalto University, Helsinki, Finland
- Helsinki Institute for Information Technology (FI)
- Venue
- Proceedings of the Ninth ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2016-02-08
- Pages
- 33-42
- OpenAlex
- W1929853451
- Cited by
- 147
- References
- 28
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 6 · 2023: 8 · 2022: 12 · 2021: 13 · 2020: 10 · 2019: 10 · 2018: 31 · 2017: 35 · 2016: 11 · 2015: 2
- FWCI
- 91.94
- Type
- conference paper
- Language
- en
- Open access
- green
- research.aalto.fi
- Locations
- ACM International Conference on Web Search and Data Mining (WSDM) (publishedVersion)
- Aaltodoc (Aalto University) (submittedVersion, other-oa)
- Funders
- SoBigData: Social Mining & Big Data Ecosystem (654024)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- controversy detection, social media, echo chambers, network structure, network content, online debate
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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