Factors affecting Consumer Brand Sabotage virality: a study of an Indian brand #boycott

Research on negative brand relationships has gained traction over the years. However, most of this research has focussed on the cases involving product or service failure by a firm, leaving conflicts arising as a result of values and ethics espoused by a firm relatively unexplored. In this research, we focus on one such case and explore the factors that influence virality of social media content in the context of Consumer Brand Sabotage (CBS). Based on a week of Twitter data pertaining to specific hashtags associated with a brand’s sabotage, we explore how tweet related attributes affect the potential for amplification of the tweets related to the event. We categorize the factors as informational, interactional, and creator specific, and build machine learning (ML) models to predict the retweet likelihood of CBS tweets. We find that while informational factors associated with the tweets (such as, hashtags, URLs and emotions) are important to predict the diffusion of CBS-related tweets, this was not the case for interactional factors (such as, reply, like, quote, etc.). For creator factors, we found that considering the number of followers of the creator in the ML models reduced the predictability of diffusion of CBS-related tweets, and found verified accounts to be of little importance as well. We discuss the implications of these findings for practice and research, and present scope for future research.