Discovering value from community activity on focused question answering sites: a case study of stack overflow

By Ashton Anderson, Dan Huttenlocher, Jon M. Kleinberg, Jure Leskovec. Knowledge Discovery and Data Mining, 2012

417 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/7cd2e2dee9b27581b4ce4bcd95090e06123ae742

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Citations in the corpus

A Comprehensive Survey and Classification of Approaches for Community Question Answering (2016, methodology): “Similarly, Anderson et al. [2012] employed a logistic regression. Features derived a short time (1, 3, 24, and 72 hours) after question creation were examined with the result that even features available after only 1 hour provide enough information to the prediction. Yao et al. [2014] proposed a fam”
The Linguistic Ideologies of Deep Abusive Language Classification (2018, background): “serious approach to tackling the problem of abusive language on the site would likely want to take advantage of the site’s periodic data dumps, which provide millions of user questions, answers, votes, and favorites (Anderson et al., 2012).”
Flock: Hybrid Crowd-Machine Learning Classifiers (2015, background): “For S TACK E XCHANGE , these features are similar to those that were most predictive in a study of a question’s long-lasting value (e.g., number of comments, user reputation) [2].”
How Community Feedback Shapes User Behavior (2014, background): “The focus has primarily been on predicting user-generated ratings (Kim et al. 2006; Ghose and Ipeirotis 2007; Otterbacher 2009; Anderson et al. 2012b) and on understanding their effects at Copyright c© 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org).”
What Do You Mean Exactly?: Analyzing Clarification Questions in CQA (2017, background): “For example, Anderson et al. [2] showed that long-term value of a question and its answers on Stack Overﬂow was positively correlated with the number of comments on the answers and the time for highest-score answer to arrive.”
Dynamics of Content Quality in Collaborative Knowledge Production (2017, background): “Such behavior could be explained by the fact that high reputation users answer questions ﬁrst (Anderson et al .”