How social media effects shape sentiments along the twitter journey?A Bayesian network approach

Consistent with market orientation, marketing activities based on sentiment analysis involve gathering market intelligence, disseminating it firmwide, and responding to customer needs. While it has been applied in various marketing settings, antecedents of consumer sentiments that are rooted in social media platform effects need further examination. Using consumers’ social media journey as conceptual support, the current research investigates how consumer sentiments on social media are shaped by hashtag position, the bandwagon effect, and user anonymity and authority. The conceptual framework presented proposes that individual user characteristics of anonymity and authority influence platform effects including hashtag position and positive and negative bandwagon effects which impact expressed sentiments. The hypotheses are tested by analyzing over half a million tweets for 127 movies using the machine learning methodology of Bayesian Network analysis. Managerial implications for regulating platform effects to achieve the intended sentiment outcome, and limitations and avenues for future research are offered.