The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
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
Zachary, W. W. (1977) · Journal of Anthropological Research · doi:10.1086/jar.33.4.3629752 · Cited by 4,709 · cited 3×
“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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“The graph layout is produced by Gephi’s ForceAtlas2 algorithm [16], and is based solely on the structure of the graph, not on the partitioning computed by METIS.”
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Filter Bubbles, Echo Chambers, and Online News Consumption
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“This phenomenon has been quantified in many recent studies [3, 11, 14].”
ResultJeh, G., & Widom, J. (2002) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/775047.775126 · Cited by 1,954 · cited 1×
“We explore the use of the all pairs expected hitting time computed using sim rank [18].”
MethodPerformance evaluation of some clustering algorithms and validity indices
Maulik, U., & Bandyopadhyay, S. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1114856 · Cited by 1,396 · cited 1×
“We use KL divergence as a measure of distance between the vocabularies of the two clusters, and the I2 measure [23] of clustering heterogeneity.”
MethodThe Consequences of Cross-Cutting Networks for Political Participation
Mutz, D. C. (2002) · American Journal of Political Science · doi:10.2307/3088437 · Cited by 1,265 · cited 1×
“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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Regressing Controversy of Music Artists from Microblogs
Hamad, M. M., Skowron, M., & Schedl, M. (2018) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2018.00090 · Cited by 4
“In [15] propose a graph-based”
Quantifying and Bursting the Online Filter Bubble
Garimella, K. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3024933 · Cited by 4
“…solve the problem of quantifying and bursting online filter bubbles in three phases: (i) Understanding the structure and dynamics of filter bubbles [2] - where we use the structure of the endorsement and comment graphs to identify if a discussion is polarized, (ii) Studying the dynamics of…”
BackgroundSelf-citationControversy Analysis and Detection
Dori-Hacohen, S. (2021) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/10013812.0 · Cited by 4
Cross-community affinity: A polarization measure for multi-community networks
Nair, S., & Iamnitchi, A. (2024) · Online Social Networks and Media · doi:10.1016/j.osnem.2024.100280 · Cited by 3
Badache, I., Chifu, A.-G., & Fournier, S. (2022) · Mathematics · doi:10.3390/math10050809 · Cited by 3
“Garimella et al. [23] constructed a conversation graph on a topic, then partitioned the conversation graph to identify potential points of controversy, and measured the contro-versy’s level based on the characteristics of the graph.”
Peeking into the other half of the glass : handling polarization in recommender systems.
Badami, M. (2017) · doi:10.18297/etd/2693 · Cited by 3
Predicting Shifting Individuals Using Text Mining and Graph Machine Learning on Twitter.Preprint
Albanese, F., Lombardi, L. E., Feuerstein, E., & Balenzuela, P. (2020) · arXiv (Cornell University) · Cited by 3
Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networks
Melton, J., Reid, S. E., Terejanu, G., & Krishnan, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-78548-1_19 · Cited by 2
Contradiction in Reviews: is it Strong or Low?
Badache, I., Fournier, S., & Chifu, A.-G. (2018) · HAL · Cited by 2
Linking the Dynamics of User Stance to the Structure of Online Discussions
Largeron, C., Mardale, A., & Rizoiu, M.-A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-74251-5_22 · Cited by 2
“Previous work concentrates mainly on detecting and forecasting opinion polarization based on content diffusion in online social networks [11, 27]; little work concentrates on detecting polarization dynamics.”
BackgroundPaschalides, D., Pallis, G., & Dikaiakos, M. D. (2021) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3487351.3489443 · Cited by 4
“However, such messages are typically short, noisy, and informal [13].”
BackgroundInfluentialFacebook Reactions as Controversy Proxies: Predictive Models over Italian News
Basile, A., Caselli, T., Merenda, F., & Nissim, M. (2018) · Italian Journal of Computational Linguistics · doi:10.4000/ijcol.514 · Cited by 2
“Recently, computational approaches on controversy detection (Pennacchiotti and Popescu 2010; Awadallah, Ramanath, and Weikum 2012; Mejova et al. 2014; DoriHacohen and Allan 2015; Borra et al. 2015; Lourentzou et al. 2015; Garimella et al. 2016; Coletto et al. 2017) have been developed with varying degrees of success a…”
BackgroundMethodInfluentialExposing Twitter Users to Contrarian NewsPreprint
Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10934 · Cited by 2
“As previous studies have confirmed, controversial topics create more polarized discussions, which are characterized by specific types of sharing and information dissemination patterns [5].”
BackgroundInfluentialSelf-citationLanguage in Our Time: An Empirical Analysis of HashtagsPreprint
Zhang, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.04590 · Cited by 2
“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].”
PROBABILISTIC MODELS FOR IDENTIFYING AND EXPLAINING CONTROVERSY
Jang, M. (2021) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/14219280 · Cited by 2
From Divide to Debate: A Methodological Approach to Dismantling Polarization in Social NetworksPreprint
Mustafa, Z. U., Amir, M., Mustafa, M., & Anwar, M. A. (2023) · Preprints.org · doi:10.20944/preprints202309.1350.v1 · Cited by 2
CONSPIRACY THEORIES AND DISINFORMATION AS TOOLS FOR UNDERMINING NATIONAL SECURITY
Životić, I., & Obradović, D. (2024) · SCIENCE International Journal · doi:10.35120/sciencej0302075z · Cited by 1
Análisis crítico del modelo BERT en la construcción de controversias públicas: el caso Hamás-Israel
Lopez, V. M. H., & Cuellar, J. E. (2025) · Signo y Pensamiento · doi:10.11144/javeriana.syp44.acmb · Cited by 1
Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter
Lahoti, P., Garimella, K., & Gionis, A. (2017) · MPG.PuRe (Max Planck Society) · doi:10.48550/arxiv.1711.10251 · Cited by 1
Self-citationControversies in public and private on-line communication
Corallo, A., Fortunato, L., Renna, C., Sarcinella, M. L., Spennato, A., & Blasi, C. D. (2020) · Controversies · doi:10.1075/cvs.16.01cor · Cited by 1
A Heterophily-Based Polarization Measure for Multi-community Networks
Nair, S., & Iamnitchi, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19097-1_32 · Cited by 1
The Italian Social Mood on Economy Index During the Covid-19 Crisis
Righi, A., Catanese, E., Valentino, L., & Zardetto, D. (2022) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-031-16609-9_29 · Cited by 1
Characterizing Controversiality of Topics Utilizing Eccentricity of Opinions
Pandey, S., & Sayama, H. (2023) · Northeast Journal of Complex Systems · doi:10.22191/nejcs/vol5/iss1/2 · Cited by 3
“[14] use content and network structure of social networks to quantify controversy.”
From Gamergate to FIFA: Identifying Polarized Groups in Online Social Media
Warmsley, D., Xu, J., & Lu, T.-C. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8621869 · Cited by 1
“[6]), how to quantify it ( [7], [8], [9]), and how to mitigate it [10].”
BackgroundMethodWikipedia Polarization and Its Effects on Navigation Paths
Menghini, C., Anagnostopoulos, A., & Upfal, E. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005566 · Cited by 1
Tackling the RecSys Side Effects via Deep Learning Approaches
Coppolillo, E. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-42941-5_57 · Cited by 1
DSP: A Statistically-Principled Structural Polarization Measure
Preti, G., Riondato, M., Gionis, A., & De Francisci Morales, G. (2026) · Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining · doi:10.1145/3773966.3777942
Self-citationIs Climate Change Controversial? Modeling Controversy as Contention Within PopulationsPreprint
Dori-Hacohen, S., Jang, M., & Allan, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10111
“The differentiation between overlapping and mutually exclusive stances might be useful for classification of controversiality, reminiscent of a recent partitioning approach to measuring controversy (Garimella et al. 2016).”
BackgroundInfluentialClassifying Controversiality in Article Data
Kaplun, K. (2018)
Social–Spatiotemporal Analysis of Topical and Polarized Communities in Online Social Networks
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Reducing Controversy by Connecting Opposing ViewsPreprint
Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.00172
“In particular, given a metric that measures how controversial an issue discussed on social media is, our goal is to find a small number of edges, called bridges, that minimize this measure.”
BackgroundSelf-citationMachine Learning Evaluation of the Echo-Chamber Effect in Medical Forums.Preprint
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Linking User Opinion Dynamics and Online DiscussionsPreprint
Largeron, C., Mardale, A., & Rizoiu, M.-A. (2021) · arXiv (Cornell University)
Social–Spatiotemporal Analysis of Topical and Polarized Communities in Online Social Networks
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Algorithmic Intervention: Addressing Social Media Polarization Via Genetic AlgorithmsPreprint
Mustafa, Z. U. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4814800
The Reality and Feature Analysis of Information Diffusion in Japanese Disinformation Examples
Ippa, S., Okubo, T., & Hashimoto, M. (2024) · Asia Joint Conference on Information Security (AsiaJCIS) · doi:10.1109/asiajcis64263.2024.00030
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“Social information, including interactions with followers and network acquaintances, yields profound insights into individual perspectives across diverse subjects (Garimella et al., 2016; Gu et al., 2017; Lotan & Minkov, 2023; Quraishi et al., 2018; Xiao et al., 2020).”
InfluentialIdeal Online Discussant and Audience: Understanding Political Social Media through Roles and Rules
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Who is at Risk? Analyzing the Risk of Radicalization Among Reddit Users
Calikus, E., Morales, G. D. F., & Gionis, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06129-4_22
Self-citationModeling and Evaluating the Impact of Extreme Opinions on Opinion Polarization in Social Groups
Liu, Y., Nakajima, K., & Aida, M. (2025) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc68382.2025.00032
Computational approaches to controversy detection: a systematic review
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Method for Identifying Twitter Accounts that Have Changed their Opinion about Politicians
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“A somewhat unexpected result was obtained in article [19]: it turned out that for political debates, the study of retweets is more important than the other “links” mentioned.”
Metadata
- 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
- 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
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