Corporate Disclosure via Social Media
A Data Science Approach
Document identifier: oai:DiVA.org:ltu-77094
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10.1108/OIR-03-2019-0084Keyword: 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,
InformationssystemPublication year: 2020Relevant Sustainable Development Goals (SDGs):
The SDG label(s) above have been assigned by OSDG.aiAbstract: 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
Other publications
>>
Ehab K.A Mohamed
Faculty of Management Technology, German University in Cairo, Cairo, Egypt
Other publications
>>
Ahmed Elragal
Luleå tekniska universitet; Digitala tjänster och system; Information Systems
Other publications
>>
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header:
identifier: oai:DiVA.org:ltu-77094
datestamp: 2021-04-19T12:52:00Z
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recordCreationDate: 2019-12-08
identifier:
http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77094
10.1108/OIR-03-2019-0084
2-s2.0-85077802662
titleInfo:
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lang: eng
title: Corporate Disclosure via Social Media
subTitle: A Data Science Approach
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.
subject:
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lang: eng
authority: uka.se
topic:
Engineering and Technology
Electrical Engineering Electronic Engineering Information Engineering
Computer Systems
@attributes:
lang: swe
authority: uka.se
topic:
Teknik och teknologier
Elektroteknik och elektronik
Datorsystem
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lang: eng
authority: uka.se
topic:
Social Sciences
Media and Communications
Information Systems Social aspects
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lang: swe
authority: uka.se
topic:
Samhällsvetenskap
Medie- och kommunikationsvetenskap
Systemvetenskap informationssystem och informatik med samhällsvetenskaplig inriktning
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lang: eng
topic: Social Media
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lang: eng
topic: Topic Modeling
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topic: Corporate Disclosure
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lang: eng
topic: Board Structure
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lang: eng
topic: LDA
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lang: eng
authority: ltu
topic: Information systems
genre: Research subject
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lang: swe
authority: ltu
topic: Informationssystem
genre: Research subject
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publication/journal-article
ref
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Published
3
Validerad;2020;Nivå 2;2020-03-05 (johcin)
name:
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Amin
Marian H.
role:
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affiliation: Faculty of Management Technology German University in Cairo Cairo Egypt
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Mohamed
Ehab K.A
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affiliation: Faculty of Management Technology German University in Cairo Cairo Egypt
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Elragal
Ahmed
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Luleå tekniska universitet
Digitala tjänster och system
Information Systems
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ahmelr
0000-0003-4250-4752
originInfo:
dateIssued: 2020
publisher: Emerald Group Publishing Limited
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title: Online information review (Print)
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1468-4527
1468-4535
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type: volume
number: 44
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type: issue
number: 1
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