Effects of Anonymity on Comment Persuasiveness in Wikipedia Articles for Deletion Discussions
Yimin Xiao | Lu Xiao
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
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.
TV-AfD: An Imperative-Annotated Corpus from The Big Bang Theory and Wikipedia’s Articles for Deletion Discussions
Yimin Xiao | Zong-Ying Slaton | Lu Xiao
Proceedings of the Twelfth Language Resources and Evaluation Conference
In this study, we created an imperative corpus with speech conversations from dialogues in The Big Bang Theory and with the written comments in Wikipedia’s Articles for Deletion discussions. For the TV show data, 59 episodes containing 25,076 statements are used. We manually annotated imperatives based on the annotation guideline adapted from Condoravdi and Lauer’s study (2012) and used the retrieved data to assess the performance of syntax-based classification rules. For the Wikipedia AfD comments data, we first developed and leveraged a syntax-based classifier to extract 10,624 statements that may be imperative, and we manually examined the statements and then identified true positives. With this corpus, we also examined the performance of the rule-based imperative detection tool. Our result shows different outcomes for speech (dialogue) and written data. The rule-based classification performs better in the written data in precision (0.80) compared to the speech data (0.44). Also, the rule-based classification has a low-performance overall for speech data with the precision of 0.44, recall of 0.41, and f-1 measure of 0.42. This finding implies the syntax-based model may need to be adjusted for a speech dataset because imperatives in oral communication have greater syntactic varieties and are highly context-dependent.