Estimating the Impact of User Personality Traits on Word-of-Mouth:
Text-mining Microblogging Platforms
Goizueta Business School at Emory University
Friday November 10, 3pm
Math & Science Center, W201
Word-of-mouth (WOM) plays an increasingly important role in shaping consumers’ online behavior and preferences as users’ opinions and choices are frequently shared in social media. In this paper, we examine whether latent personality traits of online users accentuate or attenuate the effectiveness of WOM in social media platforms. To answer this question, we leverage data-mining and machine-learning methods in combination with econometric techniques utilizing a quasi-experiment. 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 from a recipient of WOM message after exposure to the WOM message of the sender. 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 pairwise combinations of personality characteristics of senders and recipients of WOM messages on the effectiveness of WOM. For instance, introvert users are responsive to WOM, in contrast to extrovert users. Besides, agreeable, conscientious, and open social media users are more effective disseminators of WOM. In addition, WOM originating from users with low levels of emotional range affects similar users whereas for high levels of emotional range increased similarity has usually the opposite effect. The examined effects are also of significant economic importance as, for instance, a WOM message from an extrovert microblogging user to an introvert peer increases the likelihood of a subsequent purchase by 71.28%. By examining these effects and illustrating how companies can leverage the abundance of unstructured data in social media and tap into users’ latent personality characteristics, our paper provides actionable insights regarding the future potential of social media advertising and advanced micro-targeting based on big data and deep learning.
Panagiotis Adamopoulos is an Assistant Professor in the Goizueta Business School at Emory University. His research focuses on personalization, mobile and social commerce, and online education. Much of this research is grounded in big data employing data science and machine-learning techniques to leverage the abundance of unstructured data in social media, while combining these approaches with more conventional econometric and other quantitative methods as well as experimental research designs. His research has appeared in peer-reviewed academic journals and conferences, including ACM Transactions on Intelligent Systems and Technology (ACM TIST), ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), AIS International Conference on Information Systems (ICIS), and ACM Conference on Recommender Systems (RecSys).