From theory to practice: insights and hurdles in collecting social media data for social science research.

Instagram application programming interfaces data collection data ethics landscape research secondary data social media visual methods

Journal

Frontiers in big data
ISSN: 2624-909X
Titre abrégé: Front Big Data
Pays: Switzerland
ID NLM: 101770603

Informations de publication

Date de publication:
2024
Historique:
received: 12 03 2024
accepted: 08 05 2024
medline: 14 6 2024
pubmed: 14 6 2024
entrez: 14 6 2024
Statut: epublish

Résumé

Social media has profoundly changed our modes of self-expression, communication, and participation in public discourse, generating volumes of conversations and content that cover every aspect of our social lives. Social media platforms have thus become increasingly important as data sources to identify social trends and phenomena. In recent years, academics have steadily lost ground on access to social media data as technology companies have set more restrictions on Application Programming Interfaces (APIs) or entirely closed public APIs. This circumstance halts the work of many social scientists who have used such data to study issues of public good. We considered the viability of eight approaches for image-based social media data collection: data philanthropy organizations, data repositories, data donation, third-party data companies, homegrown tools, and various web scraping tools and scripts. This paper discusses the advantages and challenges of these approaches from literature and from the authors' experience. We conclude the paper by discussing mechanisms for improving social media data collection that will enable this future frontier of social science research.

Identifiants

pubmed: 38873281
doi: 10.3389/fdata.2024.1379921
pmc: PMC11169574
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1379921

Informations de copyright

Copyright © 2024 Chen, Sherren, Lee, McCay-Peet, Xue and Smit.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Yan Chen (Y)

School for Resource and Environmental Studies, Dalhousie University, Halifax, NS, Canada.

Kate Sherren (K)

School for Resource and Environmental Studies, Faculty of Management, Dalhousie University, Halifax, NS, Canada.

Kyung Young Lee (KY)

Rowe School of Business, Faculty of Management, Dalhousie University, Halifax, NS, Canada.

Lori McCay-Peet (L)

Nova Scotia Department of Cyber Security and Digital Solutions, Halifax, NS, Canada.

Shan Xue (S)

Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.

Michael Smit (M)

School of Information Management, Faculty of Management, Dalhousie University, Halifax, NS, Canada.

Classifications MeSH