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

@article{Samory2017SizingUT,
  title={Sizing Up the Troll: A Quantitative Characterization of Moderator-Identified Trolling in an Online Forum},
  author={Mattia Samory and Enoch Peserico},
  journal={Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems},
  year={2017}
}
  • Mattia Samory, E. Peserico
  • Published 2 May 2017
  • Computer Science
  • Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
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… 

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References

SHOWING 1-10 OF 25 REFERENCES
Hunting for Troll Comments in News Community Forums
TLDR
In this work, two classifiers are built that can distinguish a post by such a paid troll from one by a non-troll with 81-82% accuracy; the same classifier achieves 81- 82% accuracy on so called mentioned troll vs. non-Troll posts.
Do Not Feel The Trolls
TLDR
The aim of this work is to use sentic computing, a new paradigm for the affective analysis of natural language text, to detect trolls and hence prevent web-users from being emotionally hurt by malicious posts.
“I refuse to respond to this obvious troll":an overview of responses to (perceived) trolling
Computer-mediated communication (CMC) provides many benefits, including quick, efficient communication over time and space. At the same time, however, the anonymity it offers can give a sense of
Beyond vandalism: Wikipedia trolls
TLDR
It is concluded that Wikipedia trolls are one type of hacker, and boredom, attention seeking, and revenge motivate trolls; they regard Wikipedia as an entertainment venue, and find pleasure from causing damage to the community and other people.
Trolls Identification within an Uncertain Framework
TLDR
This work proposes a new approach for detecting malicious people also called 'Trolls' in order to allow community managers to take their ability to post online and tries to detect the presence of such malicious users.
“Uh. . . . not to be nitpicky,,,,,but…the past tense of drag is dragged, not drug.”: An overview of trolling strategies
TLDR
Initial findings suggest that trolling is perceived to broadly fall across a cline with covert strategies and overt strategies at each pole, and a working taxonomy of perceived strategies that occur at different points along this cline is created.
Trolling in asynchronous computer-mediated communication: From user discussions to academic definitions
Abstract Whilst computer-mediated communication (CMC) can benefit users by providing quick and easy communication between those separated by time and space, it can also provide varying degrees of
Antisocial Behavior in Online Discussion Communities
TLDR
This paper characterize antisocial behavior in three large online discussion communities by analyzing users who were banned from these communities, finding that such users tend to concentrate their efforts in a small number of threads, are more likely to post irrelevantly, and are more successful at garnering responses from other users.
Content attribution ignoring content
TLDR
This work tackles authorship analysis through features that ignore the explicit content of a contribution -- informally, those that can be computed even if every character in the body of a message (but not metadata such as timing or "likes") is replaced by an X.
Searching for Safety Online: Managing "Trolling" in a Feminist Forum
TLDR
The analysis suggests that feminist and other nonmainstream online forums are especially vulnerable, in that they must balance inclusive ideals against the need for protection and safety, a tension that can be exploited by disruptive elements to generate intragroup conflict.
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