skip to main content
10.1145/3025453.3026007acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

Sizing Up the Troll: A Quantitative Characterization of Moderator-Identified Trolling in an Online Forum

Published:02 May 2017Publication History

ABSTRACT

A few troublemakers often spoil online environments for everyone else. An extremely disruptive type of abuser is the troll, whose malicious activities are relatively non-obvious, and thus difficult to detect and contain -- particularly by automated systems. A growing corpus of qualitative research focuses on trolling, and differentiates it from other forms of abuse; however, its findings are not directly actionable into automated systems. On the other hand, quantitative research uses definitions of "troll" that mostly fail to capture what moderators and users consider trolling. We address this gap by giving a quantitative analysis of posts, conversations, and users, specifically sanctioned for trolling in an online forum. Although trolls (unlike most other abusers) hardly stand out in a conversation e.g. in terms of vocabulary, textit{how} they interact, rather than textit{what} they contribute, provides cues of their malicious intent.

References

  1. Jonathan Bishop. 2014. Representations of 'trolls' in mass media communication: a review of media-texts and moral panics relating to 'internet trolling'. International Journal of Web Based Communities 10, 1 (2014), 7. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Jonathan Bishop. 2016. Trolling Is Not Just a Art. It Is an Science. In Handbook of Research on Digital Crime, Cyberspace Security, and Information Assurance. Number July. IGI Global, Chapter 28, 436--450.Google ScholarGoogle Scholar
  3. Leo Breiman. 2001. Random forests. Machine learning (2001), 5--32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Erin E. Buckels, Paul D. Trapnell, and Delroy L. Paulhus. 2014. Trolls just want to have fun. Personality and Individual Differences 67 (9 2014), 97--102.Google ScholarGoogle Scholar
  5. Catherine Buni and Soraya Chemaly. 2016. The Secret Rules of the Internet: The Murky History of Moderation, and How It's Shaping the Future of Free Speech. (2016). https://goo.gl/mG2jDAGoogle ScholarGoogle Scholar
  6. Erik Cambria, Praphul Chandra, Avinash Sharma, and Amir Hussain. 2010. Do Not Feel the Trolls. In Proceedings of the 3rd International Workshop on Social Data on the Web (SDoW 2010). http://ceur-ws.org/Vol-664/paper1.pdfGoogle ScholarGoogle Scholar
  7. Justin Cheng, Cristian Danescu-Niculescu-Mizil, and Jure Leskovec. 2014. How community feedback shapes user behavior. In Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media ICWSM '14. 41--50. http://arxiv.org/abs/1405.1429Google ScholarGoogle ScholarCross RefCross Ref
  8. Justin Cheng, Cristian Danescu-Niculescu-Mizil, and Jure Leskovec. 2015. Antisocial Behavior in Online Discussion Communities. In Proceedings of the Ninth International AAAI Conference on Web and Social Media ICWSM '15. http://arxiv.org/abs/1504.00680Google ScholarGoogle Scholar
  9. Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013. A computational approach to politeness with application to social factors. Proceedings of ACL (2013). http://politeness.mpi-sws.org/Google ScholarGoogle Scholar
  10. Imen Ouled Dlala, Dorra Attiaoui, Arnaud Martin, and Boutheina Ben Yaghlane. 2014. Trolls Identification within an Uncertain Framework. In 2014 IEEE 26th International Conference on Tools with Artificial Intelligence. IEEE, 1011--1015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Judith Donath. 1999. Idenity and Deception in the Virtual Community. Communities in Cyberspace (1999), 27--58.Google ScholarGoogle Scholar
  12. Patxi Galán-García, José Gaviria De La Puerta, Carlos Laorden Gómez, Igor Santos, and Pablo García Bringas. 2014. Supervised machine learning for the detection of troll profiles in twitter social network: Application to a real case of cyberbullying. Logic Journal of the IGPL 24, 1 (2014), 42--53.Google ScholarGoogle Scholar
  13. Claire Hardaker. 2010. Trolling in asynchronous computer-mediated communication: From user discussions to academic definitions. Journal of Politeness Research 6, 2 (2010), 215--242.Google ScholarGoogle ScholarCross RefCross Ref
  14. Claire Hardaker. 2013. "Uh. . . . not to be nitpicky,but...the past tense of drag is dragged, not drug.: An overview of trolling strategies. Journal of Language Aggression and Conflict 1, 1 (2013), 58--86.Google ScholarGoogle ScholarCross RefCross Ref
  15. Claire Hardaker. 2015. "I refuse to respond to this obvious troll": an overview of responses to (perceived) trolling. Corpora 10, 2 (8 2015), 201--229.Google ScholarGoogle Scholar
  16. Susan Herring, Kirk Job-Sluder, Rebecca Scheckler, and Sasha Barab. 2002. Searching for Safety Online: Managing "Trolling" in a Feminist Forum. The Information Society 18, 5 (2002), 371--384.Google ScholarGoogle ScholarCross RefCross Ref
  17. Todor Mihaylov and Preslav Nakov. 2016. Hunting for Troll Comments in News Community Forums. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (2016), 399--405. http://anthology.aclweb.org/P16--2065Google ScholarGoogle ScholarCross RefCross Ref
  18. Paul R. Rosenbaum and Donald B. Rubin. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 1 (1983), 41--55.Google ScholarGoogle ScholarCross RefCross Ref
  19. Mattia Samory and Enoch Peserico. 2016. Content attribution ignoring content. In Proceedings of the 8th ACM Conference on Web Science - WebSci '16. ACM Press, New York, New York, USA, 233--243. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Pnina Shachaf and Noriko Hara. 2010. Beyond vandalism: Wikipedia trolls. Journal of Information Science 36, 3 (2010), 357--370. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Edgar A. Smith and R J. Senter. 1967. Automated readability index. AMRL-TR. Aerospace Medical Research Laboratories (U.S.) (5 1967), 1--14. http://www.dtic.mil/dtic/tr/fulltext/u2/667273. pdfGoogle ScholarGoogle Scholar
  22. Yla R. Tausczik and James W. Pennebaker. 2010. The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology 29, 1 (2010), 24--54.Google ScholarGoogle ScholarCross RefCross Ref
  23. Hyeongseok Wi and Wonjae Lee. 2014. The norm of normlessness: Structural Correlates of A Trolling Community. In Proceedings of the 2014 ACM conference on Web science - WebSci '14. ACM Press, New York, New York, USA, 275--276. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Sizing Up the Troll: A Quantitative Characterization of Moderator-Identified Trolling in an Online Forum

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
      May 2017
      7138 pages
      ISBN:9781450346559
      DOI:10.1145/3025453

      Copyright © 2017 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 2 May 2017

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      CHI '17 Paper Acceptance Rate600of2,400submissions,25%Overall Acceptance Rate6,199of26,314submissions,24%

      Upcoming Conference

      CHI '24
      CHI Conference on Human Factors in Computing Systems
      May 11 - 16, 2024
      Honolulu , HI , USA

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader