Exploring Anti-rumor Behaviors in Mega Projects on Sina Weibo: A Text Mining Analysis

Conflicts caused by mega projects (MPs) always generate strong public opposition and pose significant threats to social harmony. Combined with ubiquitous online access, the spread of online rumors is unparalleled. However, rumor rebuttal on social media has been largely overlooked in engineering sociology. This study attempts a cross-disciplinary approach by crawling data related to anti-rumors of a representative MP from Sina Weibo to identify anti-rumor topics and online stakeholders, and investigates the effectiveness of anti-rumor strategies. The results suggest that the sentiment-based echo chamber effect is not significantly present in both participant and strategy networks. Anti-rumor messages of traditional media and elites are effective, while that of self-media are mainly ineffective. Meanwhile, anti-rumor strategies have different effectiveness in three frameworks. Refutation and guide strategies are effective in the assessment and risk perception framework, sarcastic and disbelief strategies are counterproductive in the risk perception and progress framework, while interrogatory strategy has opposite effects in the assessment and risk perception framework. This research can contribute to developing a systematic understanding of anti-rumor communication and provide recommendations for authorities to intervene social conflicts caused by MPs.