Understanding user‐generated questions in social Q&A: A goal‐framing approach

Qian Wu, Chei Sian Lee and Dion Hoe‐Lian Goh

Journal of the Association for Information Science & Technology, 2023, vol. 74, issue 8, 990-1009

Abstract: In social Q&A, user‐generated questions can be viewed as goal expressions shaping the responses. Several studies have identified askers' goals from questions. However, it remains unclear how questions set goals for responders. To fill this gap, this research applies goal‐framing theory. Goal‐frames influence responses by attracting responders' attention to different goals. Eight question cues are used to identify gain, hedonic and normative goal‐frames. A total of 14,599 posts are collected. To investigate the influence of goal‐frames, response networks are constructed. Results reveal that gain goal‐frames attract interactions with questions, while hedonic, and normative goal‐frames promote interactions among responses. Further, topic types influence the effects of goal‐frames. Gain goal‐frames increase interactions with questions in Science, Technology, Engineering, and Mathematics (STEM) topics while hedonic and normative goal‐frames attract interactions in non‐STEM topics. This research leverages responders' perspectives to explain responses to questions, which are influenced by the goals set up by question cues. Beyond that, our findings enrich the empirical knowledge of social Q&A topics, revealing that the influence of questions varies across STEM and non‐STEM topics because the question cues for specifying goals are different in the two topics. Our research opens new directions to investigate questions from responders' perspectives.

Date: 2023
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