A Survey on Troll Detection

By M. Tomaiuolo, Gianfranco Lombardo, Monica Mordonini, S. Cagnoni, Agostino Poggi. 2020

36 citations recorded by the source corpus.

A troll is usually defined as somebody who provokes and offends people to make them angry, who wants to dominate any discussion or who tries to manipulate people’s opinions. The problems caused by such persons have increased with the diffusion of social media. Therefore, on the one hand, press bodies and magazines have begun to address the issue and to write articles about the phenomenon and its related problems while, on the other hand, universities and research centres have begun to study the features characterizing trolls and to look for solutions for their identification. This survey aims at introducing the main researches dedicated to the description of trolls and to the study and experimentation of methods for their detection.

Subfields: Information Systems, Sociology and Political Science, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage (2022, background): “Unfortunately, such exchanges in the wild will not always involve human utterances that are benign or of high quality, and will include a mixture of engaged users (dubbed helpers) and unengaged or even malicious users (dubbed trolls, following the term used elsewhere (Shachaf and Hara, 2010; Mihaylo”
Learning from data in the mixed adversarial non-adversarial case: Finding the helpers and ignoring the trolls (2022, background): “…the problem of adversarial interactions can be exacerbated because it is known that certain groups of humans can behave poorly towards bots in real life deployments (Wor-swick, 2018; Wolf et al., 2017), where we refer to such humans as “trolls” (Tomaiuolo et al., 2020; Shachaf and Hara, 2010).”