The dark and light sides of engagement: an analysis of user-generated content in online wildlife trade communities

Recent research has focused on the role of user-generated content (UGC) in the dark side of engagement on social media. In this study, we apply this to the unique context of the online exotic wildlife trade, a critical area of research due its involvement in devastating global species loss as well as harms to human health and livelihoods. We first conduct qualitative analysis on a large data set of UGC with the automatic machine-learning lexical software Leximancer 4.5. 1 to explore the discourse that occurs in comments of posts that promote behaviour change and demand reduction. Then, we complement this by testing an extended elaboration likelihood model to determine the nature of information processing that leads to positive comment valences. Our results show that motivation, opportunity and ability factors moderate the relationship between dual-processing routes and comment valence as well as influencing the likelihood of positive comment valences that indicate attitude change. This work extends the use of theory from Information Systems and Marketing to conservation and provides both conceptual and practical recommendations to encourage behaviour change and reduce the harmful effects of engagement.