Corporate disclosure via social media: a data science approach 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.,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.,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.,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 tweetsxe2x80x99 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 nonexecutive directors relating to disclosure decisions.

Exploring corporate governance research in accounting journals through latent semantic and topic analyses The literature on corporate governance (CG) has been expanding at an unprecedented rate since major corporate scandals surfaced, such as Enron, WorldCom, and HealthSouth. Corresponding with accountingu0027s important role in CG, accounting scholars increasingly have investigated CG in recent years, so the body of literature is growing. Although previous attempts have been made to summarize extant literature on CG via reviews, none of these attempts has utilized recent developments in text analyses and natural language processing. This study uses latent semantic and topic analyses to address this research gap by analysing abstracts from 1,399 articles in all accounting journals that the Australian Business Deans Council (ABDC) has rated A and A*. The ABDC journal list is widely recognized as a journalxe2x80x90quality indicator across many universities worldwide. The analyses revealed 10 distinct research topics on CG in the ABDCu0027s top accounting journals. The results presented include the five most representative articles for each topic, as distinguished by topic scores. This study carries important practice and policy implications, as it reveals major research streams and exhibits how researchers respond to various CG problems.