Corporate disclosure via social media: a data science approach.

Purpose: The purpose of this paper is to investigate corporate financial disclosure via Twitter among the top listed 350 companies in the UK as well as identify the determinants of the extent of social media usage to disclose financial information. Design/methodology/approach: This study applies an unsupervised machine learning technique, namely, Latent Dirichlet Allocation topic modeling to identify financial disclosure tweets. Panel, Logistic and Generalized Linear Model Regressions are also run to identify the determinants of financial disclosure on Twitter focusing mainly on board characteristics. Findings: Topic modeling results reveal that companies mainly tweet about 12 topics, including financial disclosure, which has a probability of occurrence of about 7 percent. Several board characteristics are found to be associated with the extent of Twitter usage as a financial disclosure platform, among which are board independence, gender diversity and board tenure. Originality/value: The extensive literature examines disclosure via traditional media and its determinants, yet this paper extends the literature by investigating the relatively new disclosure channel of social media. This study is among the first to utilize machine learning, instead of manual coding techniques, to automatically unveil the tweets' topics and reveal financial disclosure tweets. It is also among the first to investigate the relationships between several board characteristics and financial disclosure on Twitter; providing a distinction between the roles of executive vs non-executive directors relating to disclosure decisions. [ABSTRACT FROM AUTHOR]