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Abstract

Analyzing and documenting human information behaviors in the context of global public health crises such as the COVID-19 pandemic are critical to informing crisis management. Drawing on the Elaboration Likelihood Model, this study investigates how three types of peripheral cues-content richness, emotional valence, and communication topic-are associated with COVID-19 information sharing on Twitter. We used computational methods, combining Latent Dirichlet Allocation topic modeling with psycholinguistic indicators obtained from the Linguistic Inquiry and Word Count dictionary to measure these concepts and built a research model to assess their effects on information sharing. Results showed that content richness was negatively associated with information sharing. Tweets with negative emotions received more user engagement, whereas tweets with positive emotions were less likely to be disseminated. Further, tweets mentioning advisories tended to receive more retweets than those mentioning support and news updates. More importantly, emotional valence moderated the relationship between communication topics and information sharing-tweets discussing news updates and support conveying positive sentiments led to more information sharing; tweets mentioning the impact of COVID-19 with negative emotions triggered more sharing. Finally, theoretical and practical implications of this study are discussed in the context of global public health communication.

Source

Journal of the Association for Information Science and Technology. 73(2022) no.6, S.847-862

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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.

Source

Journal of the Association for Information Science and Technology. 74(2023) no.8, S.990-1009