ABSTRACT
Users organize themselves into communities on web platforms. These communities can interact with one another, often leading to conflicts and toxic interactions. However, little is known about the mechanisms of interactions between communities and how they impact users.
Here we study intercommunity interactions across 36,000 communities on Reddit, examining cases where users of one community are mobilized by negative sentiment to comment in another community. We show that such conflicts tend to be initiated by a handful of communities---less than 1% of communities start 74% of conflicts. While conflicts tend to be initiated by highly active community members, they are carried out by significantly less active members. We find that conflicts are marked by formation of echo chambers, where users primarily talk to other users from their own community. In the long-term, conflicts have adverse effects and reduce the overall activity of users in the targeted communities.
Our analysis of user interactions also suggests strategies for mitigating the negative impact of conflicts---such as increasing direct engagement between attackers and defenders. Further, we accurately predict whether a conflict will occur by creating a novel LSTM model that combines graph embeddings, user, community, and text features. This model can be used to create an early-warning system for community moderators to prevent conflicts. Altogether, this work presents a data-driven view of community interactions and conflict, and paves the way towards healthier online communities.
- Online appendix. http://snap.stanford.edu/conflict.Google Scholar
- Pytorch v0.2. http://pytorch.org/.Google Scholar
- Reddit data dump. http://files.pushshift.io/reddit/. Accessed: 2017--10--27.Google Scholar
- A. Addawood, R. Rezapour, O. Abdar, and J. Diesner. Telling apart tweets associated with controversial versus non-controversial topics. In Proceedings of the 2nd Workshop on NLP and Computational Social Science, 2017.Google ScholarCross Ref
- G. W. Allport. The nature of prejudice. Basic Books, 1979.Google Scholar
- V. Belák, S. Lam, and C. Hayes. Cross-community influence in discussion fora. ICWSM, 12:34--41, 2012.Google Scholar
- A. Binns. Don't feed the trolls! managing troublemakers in magazines' online communities. Journalism Practice, 6(4):547--562, 2012.Google ScholarCross Ref
- J. Blackburn and H. Kwak. Stfu noob!: predicting crowdsourced decisions on toxic behavior in online games. In Proceedings of the 23rd international conference on World wide web, pages 877--888. ACM, 2014. Google ScholarDigital Library
- P. Burnap and M. L. Williams. Us and them: identifying cyber hate on twitter across multiple protected characteristics. EPJ Data Science, 5(1):11, 2016.Google ScholarCross Ref
- E. Chandrasekharan, U. Pavalanathan, A. Srinivasan, A. Glynn, J. Eisenstein, and E. Gilbert. You can't stay here: The efficacy of reddit's 2015 ban examined through hate speech. In Proceedings of the ACM Human-Computer Interaction, 2017. Google ScholarDigital Library
- J. Cheng, M. Bernstein, C. Danescu-Niculescu-Mizil, and J. Leskovec. Anyone can become a troll: Causes of trolling behavior in online discussions. In Proceedings of the 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), 2017. Google ScholarDigital Library
- J. Cheng, C. Danescu-Niculescu-Mizil, and J. Leskovec. Antisocial behavior in online discussion communities. In Proceedings of the The International AAAI Conference on Web and Social Media (ICWSM), 2015.Google Scholar
- M. Conover, J. Ratkiewicz, M. R. Francisco, B. Gonccalves, F. Menczer, and A. Flammini. Political polarization on twitter. Proceedings of the 5th International AAAI Conference on Web and Social Media (ICWSM), 2011.Google Scholar
- S. Datta, C. Phelan, and E. Adar. Identifying misaligned inter-group links and communities. Proceedings of the ACM Human-Computer Interaction, 2017. Google ScholarDigital Library
- N. Djuric, J. Zhou, R. Morris, M. Grbovic, V. Radosavljevic, and N. Bhamidipati. Hate speech detection with comment embeddings. In Proceedings of the 24th International Conference on World Wide Web (WWW). ACM, 2015. Google ScholarDigital Library
- R. Faris, H. Roberts, B. Etling, N. Bourassa, E. Zuckerman, and Y. Benkler. Partisanship, propaganda, and disinformation: Online media and the 2016 us presidential election. Berkman Klein Center for Internet & Society Research Paper, 2017.Google Scholar
- E. Ferrara. Contagion dynamics of extremist propaganda in social networks. Information Sciences, 2017. Google ScholarDigital Library
- S. Fortunato. Community detection in graphs. Physics Reports, 486(3):75--174, 2010.Google ScholarCross Ref
- D. Garcia, F. Mendez, U. Serdült, and F. Schweitzer. Political polarization and popularity in online participatory media: an integrated approach. In Proceedings of the 1st Workshop on Politics, Elections and Data. ACM, 2012. Google ScholarDigital Library
- K. Garimella, G. De Francisci Morales, A. Gionis, and M. Mathioudakis. Quantifying controversy in social media. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM). ACM, 2016. Google ScholarDigital Library
- K. Garimella, G. De Francisci Morales, A. Gionis, and M. Mathioudakis. Reducing controversy by connecting opposing views. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM). ACM, 2017. Google ScholarDigital Library
- H. Giles. Intergroup communication: Multiple perspectives, volume 2. Peter Lang, 2005.Google Scholar
- J. Golbeck, Z. Ashktorab, R. O. Banjo, A. Berlinger, S. Bhagwan, C. Buntain, P. Cheakalos, A. A. Geller, Q. Gergory, R. K. Gnanasekaran, et al. A large labeled corpus for online harassment research. In Proceedings of the 2017 ACM on Web Science Conference (WebSci). ACM, 2017. Google ScholarDigital Library
- S. C. Guntuku, D. B. Yaden, M. L. Kern, L. H. Ungar, and J. C. Eichstaedt. Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences, 18:43--49, 2017.Google ScholarCross Ref
- W. Hamilton, K. Clark, J. Leskovec, and D. Jurafsky. Inducing domain-specific sentiment lexicons from unlabeled corpora. Proceedings of the 2016 Conference on Empirical Methods on Natural Language Processing (EMNLP), 2016.Google ScholarCross Ref
- W. Hamilton, J. Zhang, C. Danescu-Niculescu-Mizil, D. Jurafsky, and J. Leskovec. Loyalty in online communities. In Proceedings of 2017 The International AAAI Conference on Web and Social Media (ICWSM), 2017.Google Scholar
- W. L. Hamilton, R. Ying, and J. Leskovec. Representation learning on graphs: Methods and applications. IEEE Data Engineering Bulletin, 2017.Google Scholar
- C. Hardaker. Trolling in asynchronous computer-mediated communication: From user discussions to academic definitions. Journal of Politeness Research, 2010.Google ScholarCross Ref
- C. Hauser. Reddit bans nazi groups and others in crackdown on violent content. New York Times, October 2017. {Online; posted 26-October-2017}.Google Scholar
- M. Hewstone, M. Rubin, and H. Willis. Intergroup bias. Annual Review of Psychology, 53(1):575--604, 2002.Google ScholarCross Ref
- M. E. Hewstone and R. E. Brown. Contact and conflict in intergroup encounters. Basil Blackwell, 1986.Google Scholar
- S. Hinduja and J. W. Patchin. Bullying beyond the schoolyard: Preventing and responding to cyberbullying. Corwin Press, 2014. Google ScholarDigital Library
- G. E. Hine, J. Onaolapo, E. De Cristofaro, N. Kourtellis, I. Leontiadis, R. Samaras, G. Stringhini, and J. Blackburn. Kek, cucks, and god emperor trump: A measurement study of 4chan's politically incorrect forum and its effects on the web. In Proceedings of the The International AAAI Conference on Web and Social Media (ICWSM), 2017.Google Scholar
- S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 9(8):1735--1780, 1997. Google ScholarDigital Library
- H. Hosseinmardi, A. Ghasemianlangroodi, R. Han, Q. Lv, and S. Mishra. Towards understanding cyberbullying behavior in a semi-anonymous social network. In Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on, pages 244--252. IEEE, 2014. Google ScholarDigital Library
- C. J. Hutto and E. Gilbert. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the International AAAI Conference on Weblogs and Social Media (ICWSM), 2014.Google Scholar
- D. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv:1412.6980, 2014.Google Scholar
- R. E. Kraut, P. Resnick, S. Kiesler, M. Burke, Y. Chen, N. Kittur, J. Konstan, Y. Ren, and J. Riedl. Building successful online communities: Evidence-based social design. MIT Press, 2012. Google ScholarCross Ref
- S. Kumar, J. Cheng, J. Leskovec, and V. Subrahmanian. An army of me: Sockpuppets in online discussion communities. In Proceedings of the 26th International Conference on World Wide Web, 2017. Google ScholarDigital Library
- S. Kumar, B. Hooi, D. Makhija, M. Kumar, C. Faloutsos, and V. Subrahamanian. Rev2: Fraudulent user prediction in rating platforms. Proceedings of the 11th ACM International Conference on Web Search and Data Mining, 2018. Google ScholarDigital Library
- S. Kumar and N. Shah. False information on web and social media: A survey. In Social Media Analytics: Advances and Applications. CRC, 2018.Google Scholar
- S. Kumar, F. Spezzano, and V. Subrahmanian. Accurately detecting trolls in slashdot zoo via decluttering. In Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on, pages 188--195. IEEE, 2014. Google ScholarDigital Library
- S. Kumar, F. Spezzano, and V. Subrahmanian. Vews: A wikipedia vandal early warning system. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015. Google ScholarDigital Library
- S. Kumar, R. West, and J. Leskovec. Disinformation on the web: Impact, characteristics, and detection of wikipedia hoaxes. In Proceedings of the 25th International Conference on World Wide Web, 2016. Google ScholarDigital Library
- H. Lamba, M. M. Malik, and J. Pfeffer. A tempest in a teacup analyzing firestorms on twitter. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2015. Google ScholarDigital Library
- K. Lee, P. Tamilarasan, and J. Caverlee. Crowdturfers, campaigns, and social media: Tracking and revealing crowdsourced manipulation of social media. In ICWSM, 2013.Google Scholar
- J. Leskovec, D. Huttenlocher, and J. Kleinberg. Predicting positive and negative links in online social networks. In Proceedings of the 19th international conference on World Wide Web (WWW). ACM, 2010. Google ScholarDigital Library
- O. Levy and Y. Goldberg. Dependency-based word embeddings. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), 2014.Google ScholarCross Ref
- D. Liben-Nowell and J. Kleinberg. The link-prediction problem for social networks. Journal of the Association for Information Science and Technology (JASIST), 58(7):1019--1031, 2007. Google ScholarDigital Library
- L. v. d. Maaten and G. Hinton. Visualizing data using t-sne. Journal of Machine Learning Research (JMLR), 9(Nov):2579--2605, 2008.Google Scholar
- J. N. Matias, A. Johnson, W. E. Boesel, B. Keegan, J. Friedman, and C. DeTar. Reporting, reviewing, and responding to harassment on twitter. arXiv:1505.03359, 2015.Google Scholar
- Y. Mejova, A. X. Zhang, N. Diakopoulos, and C. Castillo. Controversy and sentiment in online news. Computation and Journalism Symposium, 2014.Google Scholar
- T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Proceedings of the Advances in Neural Information Processing Systems (NIPS), 2013. Google ScholarDigital Library
- T. Mitra and E. Gilbert. Credbank: A large-scale social media corpus with associated credibility annotations. In ICWSM, pages 258--267, 2015.Google Scholar
- K. Munger. Tweetment effects on the tweeted: Experimentally reducing racist harassment. Political Behavior, 39(3):629--649, 2017.Google ScholarCross Ref
- C. Nobata, J. Tetreault, A. Thomas, Y. Mehdad, and Y. Chang. Abusive language detection in online user content. In Proceedings of the 25th International Conference on World Wide Web (WWW), 2016. Google ScholarDigital Library
- J. G. Noel, D. L. Wann, and N. R. Branscombe. Peripheral ingroup membership status and public negativity toward outgroups. Journal of Personality and Social Psychology, 68:127--127, 1995.Google ScholarCross Ref
- U. of Oklahoma. Institute of Group Relations and M. Sherif. Intergroup conflict and cooperation: The Robbers Cave experiment, volume 10. University Book Exchange Norman, 1961.Google Scholar
- L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab, 1999.Google Scholar
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. scikit-learn: Machine learning in Python. The Journal of Machine Learning Research (JMLR), 2011. Google ScholarDigital Library
- J. Pennington, R. Socher, and C. Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014.Google ScholarCross Ref
- B. Perozzi, R. Al-Rfou, and S. Skiena. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pages 701--710. ACM, 2014. Google ScholarDigital Library
- T. F. Pettigrew. Generalized intergroup contact effects on prejudice. Personality and Social Psychology Bulletin, 23(2):173--185, 1997.Google ScholarCross Ref
- T. F. Pettigrew and L. R. Tropp. Does intergroup contact reduce prejudice? recent meta-analytic findings. Reducing Prejudice and Discrimination, 93:114, 2000.Google Scholar
- M. A. Rahim. Managing conflict in organizations. Transaction Publishers, 2010.Google Scholar
- J. Ratkiewicz, M. Conover, M. R. Meiss, B. Gonccalves, A. Flammini, and F. Menczer. Detecting and tracking political abuse in social media. Proceedings of the 5th International AAAI Conference on Web and Social Media (ICWSM), 2011.Google Scholar
- M. H. Ribeiro, P. H. Calais, V. A. Almeida, and W. Meira Jr. “everything i disagree with is# fakenews”: Correlating political polarization and spread of misinformation. arXiv:1706.05924, 2017.Google Scholar
- H. Saif, M. Fernandez, M. Rowe, and H. Alani. On the role of semantics for detecting pro-isis stances on social media. In Proceedings of the CEUR Workshop, volume 1690, 2016.Google Scholar
- G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5):513--523, 1988. Google ScholarDigital Library
- M. Sherif. Group conflict and co-operation: Their social psychology, volume 29. Psychology Press, 2015.Google Scholar
- S. Siegal. Nonparametric statistics for the behavioral sciences. McGraw-hill, 1956.Google Scholar
- L. A. Silva, M. Mondal, D. Correa, F. Benevenuto, and I. Weber. Analyzing the targets of hate in online social media. In Proceedings of the 2016 The International AAAI Conference on Web and Social Media (ICWSM), 2016.Google Scholar
- H. Tajfel. Social psychology of intergroup relations. Annual Review of Psychology, 33(1):1--39, 1982.Google ScholarCross Ref
- H. Tajfel. Social identity and intergroup relations. Cambridge University Press, 2010.Google Scholar
- H. Tajfel and J. C. Turner. An integrative theory of intergroup conflict. The Social Psychology of Intergroup Relations, 1979.Google Scholar
- C. Tan and L. Lee. All who wander: On the prevalence and characteristics of multi-community engagement. In Proceedings of International World Wide Web Conference (WWW), 2015. Google ScholarDigital Library
- Y. R. Tausczik and J. W. Pennebaker. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, Mar. 2010.Google ScholarCross Ref
- W. Wang, L. Chen, K. Thirunarayan, and A. P. Sheth. Cursing in english on twitter. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (CSCW). ACM, 2014. Google ScholarDigital Library
- E. Wulczyn, N. Thain, and L. Dixon. Ex machina: Personal attacks seen at scale. In Proceedings of the 26th International Conference on World Wide Web, pages 1391--1399. International World Wide Web Conferences Steering Committee, 2017. Google ScholarDigital Library
- J. Xie, S. Kelley, and B. K. Szymanski. Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Computing Surveys (CSUR), 45(4):43, 2013. Google ScholarDigital Library
- T. Yasseri, R. Sumi, A. Rung, A. Kornai, and J. Kertész. Dynamics of conflicts in wikipedia. PloS One, 7(6):e38869, 2012.Google ScholarCross Ref
- S. Zannettou, T. Caulfield, E. De Cristofaro, N. Kourtelris, I. Leontiadis, M. Sirivianos, G. Stringhini, and J. Blackburn. The web centipede: understanding how web communities influence each other through the lens of mainstream and alternative news sources. In Proceedings of the 2017 Internet Measurement Conference, pages 405--417. ACM, 2017. Google ScholarDigital Library
- J. Zhang, W. Hamilton, C. Danescu-Niculescu-Mizil, D. Jurafsky, and J. Leskovec. Community identity and user engagement in a multi-community landscape. In Proceedings of 2017 The International AAAI Conference on Web and Social Media (ICWSM), 2017.Google Scholar
Index Terms
- Community Interaction and Conflict on the Web
Recommendations
Understanding Community-Level Conflicts Through Reddit r/place
CSCW '20 Companion: Companion Publication of the 2020 Conference on Computer Supported Cooperative Work and Social ComputingConflicts between communities in social-networking sites can degrade quality of communication and discourage participation, so understanding conflict dynamics can aid community management. However, studying inter-community conflict is challenging due to ...
Community: issues, definitions, and operationalization on the web
WWW '12 Companion: Proceedings of the 21st International Conference on World Wide WebThis paper addresses the concepts of community and online community and discusses the physical, functional, and symbolic characteristics of a community that have formed the basis for traditional definitions. It applies a four-dimensional perspective of ...
Community Inventor Days: Scaffolding Grassroots Innovation with Maker Events
DIS '17: Proceedings of the 2017 Conference on Designing Interactive SystemsThis paper describes a series of Inventor Days designed to catalyse sustainable relationships between communities and makers to support grassroots innovation. By appropriating core properties of hackathons, the Inventor Days brought together residents ...
Comments