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
Burt, R. S. (1992) · SSRN Electronic Journal · Cited by 3,048
Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098679 · Cited by 2,909 · cited 2×
“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
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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