Search for: All records

PurposeThis study seeks to establish a new framework for categorizing incivility, differentiating between explicit and implicit forms, and to investigate their respective abilities to proliferate and mobilize conversations, along with behavioral outcomes in various social contexts. Design/methodology/approachEmploying computational techniques, this research analyzed 10,145 protest-related threads from the HK Golden Forum, a prominent online discussion board in Hong Kong. FindingsOur analysis revealed divergent effects of explicit and implicit incivility on their diffusion, influences on deliberative discussions, and user participation. Explicit incivility was found to impede deliberative conversations, while implicit incivility tended to provoke more responses. Explicit uncivil expressions encouraged the propagation of incivility but reduced the likelihood of individual involvement. In contrast, implicit incivility had a stronger dampening effect on further uncivil comments and achieved greater thread popularity. The results showed strong associations between uncivil expressions and the contextual norms surrounding social movements. Originality/valueTheoretically, this research introduced a classification of incivility and underscored the importance of differentiating between implicit and explicit incivility by examining their effects on deliberation and engagement. Although previous studies have extensively covered explicit incivility, this study goes further by analyzing implicit incivility and comparing both forms of uncivil discourse in a less-studied context. Methodologically, the study developed a Cantonese dictionary to differentiate between two types of incivility, providing a practical reference for more nuanced analyses. By revealing how varying movement norms moderate the interplay between deliberative and uncivil expressions, the study drew attention to the highly situational nature of incivility. 

Although studies have investigated cyber-rumoring previous to the pandemic, little research has been undertaken to study rumors and rumor-corrections during the COVID-19 (coronavirus disease 2019) pandemic. Drawing on prior studies about how online stories become viral, this study will fill that gap by investigating the retransmission of COVID-19 rumors and corrective messages on Sina Weibo, the largest and most popular microblogging site in China. This study examines the impact of rumor types, content attributes (including frames, emotion, and rationality), and source characteristics (including follower size and source identity) to show how they affect the likelihood of a COVID-19 rumor and its correction being shared. By exploring the retransmission of rumors and their corrections in Chinese social media, this study will not only advance scholarly understanding but also reveal how corrective messages can be crafted to debunk cyber-rumors in particular cultural contexts.