Evaluating and predicting answer quality in community QA

By Chirag Shah, Jeffrey Pomerantz. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2010

399 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/bc800d570ffb11b4f5d39156c28281f81f7100dd

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Citations in the corpus

Motivations for sharing information and social support in social media: A comparative analysis of Facebook, Twitter, Delicious, YouTube, and Flickr (2015, methodology): “Amazon MTurk has been frequently used to collect data for social media research (e.g., Asur & Huberman, 2010; Diakopoulos & Shamma, 2010; Shah & Pomerantz, 2010; Nichols, Zhou,Yang, Kang, & Sun, 2013).”
ICRC-HIT: A Deep Learning based Comment Sequence Labeling System for Answer Selection Challenge (2015, background): “Another option is extracting user metadata (Chen and Nayak, 2008; Shah and Pomerantz, 2010) from the question answering portal for enriching the feature-engineering.”
Discovering value from community activity on focused question answering sites: a case study of stack overflow (2012, background): “(under various notions of quality [21]) with respect to a par ticular question.”
AVA: an Automatic eValuation Approach to Question Answering Systems (2020, methodology): “Automatic evaluation for QA was addressed by Magnini et al. (2002) and also for multiple sub-domain QA systems (Leidner and Callison-Burch, 2003; Lin and Demner-Fushman, 2006; Shah and Pomerantz, 2010; Gunawardena et al., 2015).”
What are developers talking about? An analysis of topics and trends in Stack Overflow (2014, background): “Answers (2012), to discover patterns of user behavior (Gyöngyi et al. 2008), determine the quality of answers (Shah and Pomerantz 2010), and analyze design features of successful Q&A websites (Mamykina et al. 2011).”
How do programmers ask and answer questions on the web?: NIER track (2011, background): “In a related project, Shah and Pomer-antz [11] found that contextual information such as a user’s proﬁle can be used to predict content quality.”
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.”