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
Davies, D. L., & Bouldin, D. W. (1979) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1979.4766909 · Cited by 9,145 · cited 1×
“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.”
MethodThe political blogosphere and the 2004 U.S. election: divided they blog
Adamic, L. A., & Glance, N. (2005) · Proceedings of the 3rd international workshop on Link discovery · doi:10.1145/1134271.1134277 · Cited by 2,619
Filter Bubbles, Echo Chambers, and Online News Consumption
Flaxman, S., Goel, S., & Rao, J. M. (2016) · Public Opinion Quarterly · doi:10.1093/poq/nfw006 · Cited by 1,992 · cited 1×
“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
Conover, M., Ratkiewicz, J., Francisco, M., Goncalves, B., Flammini, A., & Menczer, F. (2011) · Zenodo · doi:10.5281/zenodo.4589064 · Cited by 1,254
Cooperative Game Theory Approaches for Network Partitioning
Avrachenkov, K. E., Kondratev, A. Y., & Mazalov, V. V. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-62389-4_49 · Cited by 1,075
Political Polarization on Twitter
Conover, M., Ratkiewicz, J., Francisco, M., et al. (2011) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4589065 · Cited by 1,005
Karypis, G. (2002) · doi:10.21236/ada439508 · Cited by 550 · cited 1×
“in the whole topic, after standard preprocessing used in IR (lowercasing, stopword removal, stemming). Following the lines of our main pipeline, we group these vectors in two clusters by using CLUTO [Karypis 2002] with cosine distance. The underlying assumption is that the two sides, while sharing the use of the hasht…”
MethodEfficient community detection in large networks using content and links
Ruan, Y., Fuhry, D., & Parthasarathy, S. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2488388.2488483 · Cited by 312
Measuring political polarization: Twitter shows the two sides of Venezuela
Morales, A. J., Borondo, J., Losada, J. C., & Benito, R. M. (2015) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4913758 · Cited by 232 · cited 2×
“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 195 · cited 2×
“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
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 80 · cited 1×
“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
An, J., Quercia, D., & Crowcroft, J. (2014) · Conference on Online Social Networks · doi:10.1145/2660460.2660469 · Cited by 71 · cited 1×
“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
Mejova, Y., Zhang, A. X., Diakopoulos, N. A., & Castillo, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.8152 · Cited by 63
Identifying Controversial Issues and Their Sub-topics in News Articles
Choi, Y., Jung, Y., & Myaeng, S.-H. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-13601-6_16 · Cited by 59 · cited 1×
“In this case, the query “#beefban” defines a topic of discussion, and the related activity consists of all posts that contain the query.”
BackgroundSocial Determinants of Content Selection in the Age of (Mis)Information
Bessi, A., Caldarelli, G., Vicario, M. D., Scala, A., & Quattrociocchi, W. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13734-6_18 · Cited by 46 · cited 1×
“Previous studies have also shown that people retweet similar ideologies but mention across ideologies [5].”
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×
“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
Doris-Down, A., Versee, H., & Gilbert, É. (2013) · International Conference on Communities and Technologies (C&T) · doi:10.1145/2482991.2483002 · Cited by 20
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.2110621 · Cited by 15 · cited 2×
“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.”
BackgroundEsterling, K., Fung, A., & Lee, T. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.1401151 · Cited by 9
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
Research works printed but not linked: 2
- G. Karypis and V. Kumar . METIS - Unstructured Graph Partitioning and Sparse Matrix Ordering System , 1995 . G. Karypis and V. Kumar. METIS - Unstructured Graph Partitioning and Sparse Matrix Ordering System, 1995.
- M. Thelwall . Heart and soul: Sentiment strength detection in the social web with sentistrength . In CyberEmotions , pages 1 -- 14 , 2013 . M. Thelwall. Heart and soul: Sentiment strength detection in the social web with sentistrength. In CyberEmotions, pages 1--14, 2013.
Cited by
Sorted by citations
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
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
“We use this approach in an earlier version of this work [20], with samples of 10,000 random walks.”
BackgroundMethodSelf-citationPolitical Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship
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
“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 375
“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
“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].”
Background
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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