Estimating the Impact of User Personality Traits on Word-of-Mouth: Text-Mining Microblogging Platforms

Abstract

Word-of-mouth (WOM) plays an increasingly important role in shaping consumers’ online behaviors and preferences as users’ opinions, choices, and decisions are frequently shared in social media. In this paper, we examine whether personality similarity between social media users can accentuate or attenuate the effectiveness of WOM leveraging data mining and machine-learning methods and the abundance of unstructured data in social media. Specifically, we study whether latent personality characteristics of users are associated with the effectiveness of WOM from purchases on social media platforms like Twitter and can predict their online economic behavior. Our analysis yields two main results. First, there is a positive and statistically significant effect of the level of personality similarity between two social media users on the likelihood of a subsequent purchase after exposure to WOM. In particular, exposure to WOM messages from similar users in terms of personality, rather than dissimilar users, increases the likelihood of a post-purchase by 47.58%. Second, there are statistically significant effects of specific personality characteristics on WOM effectiveness. For instance, users with low levels of extraversion are responsive to WOM, in contrast to extrovert users. In addition, WOM originating from users with high levels of emotional range affects similar users whereas for low levels of emotional range increased similarity has usually the opposite effect. By examining these effects and illustrating how companies can leverage the abundance of unstructured data and tap into users’ latent personality characteristics, our paper provides insights regarding the future potential of social media advertising and advanced micro-targeting based on machine learning and natural language processing approaches.