Beyond vandalism: Wikipedia trolls
By Pnina Shachaf, Noriko Hara. 2010
287 citations recorded by the source corpus.
Research on trolls is scarce, but their activities challenge online communities; one of the main challenges of the Wikipedia community is to fight against vandalism and trolls. This study identifies Wikipedia trolls’ behaviours and motivations, and compares and contrasts hackers with trolls; it extends our knowledge about this type of vandalism and concludes that Wikipedia trolls are one type of hacker. This study reports that 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. Findings also suggest that trolls’ behaviours are characterized as repetitive, intentional, and harmful actions that are undertaken in isolation and under hidden virtual identities, involving violations of Wikipedia policies, and consisting of destructive participation in the community.
Subfields: Communication, Information Systems, Sociology and Political Science, Social Sciences & Humanities
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Communication
- Jure Leskovec
- Michael S. Bernstein
- Cristian Danescu-Niculescu-Mizil
- Justin Cheng
- Jason Aaron Edward Weston
- Jing Xu
- Da Ju
- Emily Dinan
In Information Systems
- Michael S. Bernstein
- Cristian Danescu-Niculescu-Mizil
- Jure Leskovec
- Justin Cheng
- Florian Tramèr
- Matthew Jagielski
- Jason Aaron Edward Weston
- Andreas F. Terzis
In Sociology and Political Science
- Melanie Kambadur
- Cristian Danescu-Niculescu-Mizil
- Justin Cheng
- Jason Aaron Edward Weston
- Michael S. Bernstein
- Jure Leskovec
- Florian Tramèr
- Emily Dinan
In Social Sciences & Humanities
- Cristian Danescu-Niculescu-Mizil
- Justin Cheng
- Jason Aaron Edward Weston
- Michael S. Bernstein
- Megan Ung
- Jure Leskovec
- Jing Xu
- Melanie Kambadur
Citations in the corpus
- Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack (2019, background): “As such, some bad actors go to great lengths to both avoid detection and to achieve their goals (Shachaf and Hara, 2010).”
- 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”
- How Community Feedback Shapes User Behavior (2014, background): “…a growing concern (Heymann, Koutrika, and Garcia-Molina 2007), as emphasized by work on review spamming (Lim et al. 2010; Mukherjee, Liu, and Glance 2012; Ott, Cardie, and Hancock 2012), trolling (Shachaf and Hara 2010), social deviance (Shores et al. 2014) and online harassment (Yin et al. 2009).”
- Trolls just want to have fun (2014, background): “For instance, Shachaf and Hara (2010) conducted interviews of Wikipedia trolls, finding themes of boredom, attention seeking, revenge, pleasure, and a desire to cause damage to the community among their expressed motivations for trolling.”
- Poisoning Web-Scale Training Datasets is Practical (2023, background): “Indeed, it is easy to temporarily edit Wikipedia to vandalize its contents [63, 69].”
- Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions (2017, background): “Previous work has suggested several motivations for engaging in antisocial behavior: out of boredom [86], for fun [80], or to vent [55].”
- Antisocial Behavior in Online Discussion Communities (2015, background): “While some work has tried to experimentally establish causal links, for example, between personality type and trolling (Buckels, Trapnell, and Paulhus 2014), most research reports qualitative studies that focus on characterizing antisocial behavior (Donath 1999; Hardaker 2010), often by studying the”
- Learning from data in the mixed adversarial non-adversarial case: Finding the helpers and ignoring the trolls (2022, background): “, 2017), where we refer to such humans as “trolls” (Tomaiuolo et al., 2020; Shachaf and Hara, 2010).”