Predictors of answer quality in online Q&A sites

By F. Maxwell Harper, Daphne Ruth Raban, Sheizaf Rafaeli, J. Konstan. International Conference on Human Factors in Computing Systems, 2008

490 citations recorded by the source corpus.

Subfields: Communication, Information Systems

View Paper: https://www.semanticscholar.org/paper/cbb97564a8dce2719bff5bb572686e893c257681

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Citations in the corpus

Rewarding in open innovation communities – how to motivate members (2010, background): “Despite some positive results concerning rewarding and motivation (Lakhani and Wolf, 2005; Harper et al., 2008), the predominant belief appears to be that no monetary rewards are needed and only non-monetary rewarding or unexpected rewards would be satisfactory for the members.”
How useful are your comments?: analyzing and predicting youtube comments and comment ratings (2010, background): “Did the inquirer some apriori research on the topic?) is manually analyzed in [12].”
Characterizing Online Discussion Using Coarse Discourse Sequences (2017, background): “Prior studies on question-answering (Harper et al. 2008), argumentation (Tan et al. 2016), echo chambers (Gilbert, Bergstrom, and Kara-halios 2009), and gratitude (Spiro, Matias, and Monroy-Hern´andez 2016) have used datasets signiﬁcantly smaller than our dataset or focused on only one or a few…”
Evaluating and predicting answer quality in community QA (2010, methodology): “As a proxy for the original asker, we therefore used Amazon Mechanical Turk.”
A Comprehensive Survey and Classification of Approaches for Community Question Answering (2016, methodology): “Hieber and Riezler [2011] treated answer ranking problem from information retrieval perspective. A question was expanded with snippets from web search in order to calculate more precise question-answer similarity features. Consequently, pairwise SVM Rank was employed to learn how to rank answers. B.”
Combating Attacks and Abuse in Large Online Communities (2016, background): “Researchers have studied community based Q&A (CQA) sites such as Yahoo Answers [96, 35, 95, 150, 194, 195], MSN QnA [104 , 83], Stack Overflow [39, 143], Math Overflow [212] from different perspectiv es.”
What do people ask their social networks, and why?: a survey study of status message q&a behavior (2010, background): “[14] found fee-based sites produced higher quality answers than free sites.”
Crowd-Based Personalized Natural Language Explanations for Recommendations (2016, background): “While professionals may generate higher quality content { though prior work has shown counter-examples [10] { crowdsourcing has the advantages of being scalable, on-demand, and relatively inexpensive.”