Experts and likely to be closed discussions in question and answer communities: An analytical overview. (March 2019)

• Record Type: Journal Article
• Authors: Procaci, Thiago Baesso; Siqueira, Sean Wolfgand Matsui; Pereira Nunes, Bernardo; Nurmikko-Fuller, Terhi
• Is Part Of: Computers in human behavior. Volume 92(2019)
• Page Start: 519
• Page End: 535
• Publication Date: 2019-03
• Subjects: Q&A community analysis -- Expert behavior -- Likely to be closed discussions -- Interaction analysis -- Graph analysis -- Topical experts; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2018.06.004
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: How do important members of online Question & Answer communities (who we call experts ) behave? And how do they influence the discussions in which they take part? This work reports on an investigation into these questions, which we answer through analyses exploring metrics, machine learning classifiers, and recommendations. We report on several findings: the degree of expertise correlates to behavioral patterns, whereby experts would rarely ask for help, and instead, predominantly provide help to other community members; the inclusion of an expert results in longer discussions. We propose a metric (the weighted sum), which enables us to better quantify the reputations of expert members of the community. We describe the use of four machine learning classifiers for the identification of both expert users and the most significant conversations within these communities. We propose a novel approach for a recommendation system, which utilizes semantic annotations to identify topical experts and to ascertain their respective area of specialism. We foresee the suitability of our expertise-finding methods and findings to support Learning Analytics, and in scenarios where users may apply lessons learnt from our results to improve their status in a community. Our findings can also inform systems for recommending experts and discussions. Highlights: Experts gained their status mainly by providing help rather than asking for it. The weighted sum better quantifies the reputation of an expert member. The inclusion of an expert in a discussion was found to result in longer debates. Machine Learning and users' behavior identified likely to be closed discussions and experts. Semantic annotations are helpful to find and recommend a topical expert.