Effects of Anonymity on Comment Persuasiveness in Wikipedia Articles for Deletion Discussions

Yimin Xiao, Lu Xiao


Abstract
It has been shown that anonymity affects various aspects of online communications such as message credibility, the trust among communicators, and the participants’ accountability and reputation. Anonymity influences social interactions in online communities in these many ways, which can lead to influences on opinion change and the persuasiveness of a message. Prior studies also suggest that the effect of anonymity can vary in different online communication contexts and online communities. In this study, we focus on Wikipedia Articles for Deletion (AfD) discussions as an example of online collaborative communities to study the relationship between anonymity and persuasiveness in this context. We find that in Wikipedia AfD discussions, more identifiable users tend to be more persuasive. The higher persuasiveness can be related to multiple aspects, including linguistic features of the comments, the user’s motivation to participate, persuasive skills the user learns over time, and the user’s identity and credibility established in the community through participation.
Anthology ID:
2020.nlpcss-1.12
Volume:
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
Month:
November
Year:
2020
Address:
Online
Editors:
David Bamman, Dirk Hovy, David Jurgens, Brendan O'Connor, Svitlana Volkova
Venue:
NLP+CSS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
104–115
Language:
URL:
https://aclanthology.org/2020.nlpcss-1.12
DOI:
10.18653/v1/2020.nlpcss-1.12
Bibkey:
Cite (ACL):
Yimin Xiao and Lu Xiao. 2020. Effects of Anonymity on Comment Persuasiveness in Wikipedia Articles for Deletion Discussions. In Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pages 104–115, Online. Association for Computational Linguistics.
Cite (Informal):
Effects of Anonymity on Comment Persuasiveness in Wikipedia Articles for Deletion Discussions (Xiao & Xiao, NLP+CSS 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.nlpcss-1.12.pdf
Video:
 https://slideslive.com/38940619