Corporate Disclosure via Social Media

A Data Science Approach

Document identifier: oai:DiVA.org:ltu-77094
Access full text here:10.1108/OIR-03-2019-0084
Keyword: Engineering and Technology, Medie- och kommunikationsvetenskap, Information systems, LDA, Board Structure, Corporate Disclosure, Topic Modeling, Social Media, Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning, Samhällsvetenskap, Electrical Engineering, Electronic Engineering, Information Engineering, Information Systems, Social aspects, Media and Communications, Social Sciences, Datorsystem, Elektroteknik och elektronik, Teknik och teknologier, Computer Systems, Informationssystem
Publication year: 2020
Relevant Sustainable Development Goals (SDGs):
SDG 16 Peace, justice and strong institutions
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

Purpose - The aim 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 (LDA) 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 – 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 versus non-executive directors relating to disclosure decisions.

Authors

Marian H. Amin

Faculty of Management Technology, German University in Cairo, Cairo, Egypt
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Ehab K.A Mohamed

Faculty of Management Technology, German University in Cairo, Cairo, Egypt
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Ahmed Elragal

Luleå tekniska universitet; Digitala tjänster och system; Information Systems
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