A topology of Twitter research: disciplines, methods, and ethics
Abstract
Purpose – The purpose of this paper is to engage in a systematic analysis of academic research that relies on the collection and use of Twitter data, creating topology of Twitter research that details the disciplines and methods of analysis, amount of tweets and users under analysis, the methods used to collect Twitter data, and accounts of ethical considerations related to these projects. Design/methodology/approach – Content analysis of 382 academic publications from 2006 to 2012 that used Twitter as their primary platform for data collection and analysis. Findings – The analysis of over 380 scholarly publications utilizing Twitter data reveals noteworthy trends related to the growth of Twitter-based research overall, the disciplines engaged in such research, the methods of acquiring Twitter data for analysis, and emerging ethical considerations of such research. Research limitations/implications – The findings provide a benchmark analysis that must be updated with the continued growth of Twitter-based research. Originality/value – The research is the first full-text systematic analysis of Twitter-based research projects, focussing on the growth in discipline and methods as well as its ethical implications. It is of value for the broader research community currently engaged in social media-based research, and will prompt reflexive evaluation of what research is occurring, how it is occurring, what is being done with Twitter data, and how researchers are addressing the ethics of Twitter-based research.
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Zimmer and Proferes (2014) found that of 382 Twitter studies only about 5% use existing datasets collected by other researchers.
“Zimmer and Proferes (2014) found that of 382 Twitter studies only about 5% use existing datasets collected by other researchers.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Zimmer and Proferes (2014)
Cites
Social network sites: definition, history, and scholarship
boyd, D. M., & Ellison, N. B. (2007) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2007.00393.x · Cited by 16,251 · see citing works
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206 · see citing works
Reflections on #Occupy Everywhere: Social Media, Public Space, and Emerging Logics of Aggregation
Juris, J. S. (2012) · American Ethnologist · doi:10.1111/j.1548-1425.2012.01362.x · Cited by 989 · see citing works
New Media, Networking and Phatic Culture
Miller, V. (2008) · Convergence The International Journal of Research into New Media Technologies · doi:10.1177/1354856508094659 · Cited by 444 · see citing works
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BONILLA, Y., & ROSA, J. (2015) · American Ethnologist · doi:10.1111/amet.12112 · Cited by 1,116 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
“Yet, Hutton and Henderson (2015b)’s study of 505 papers mentioning a social network between 2011-2013 revealed that only about 6% shared any data, while Zimmer and Proferes (2014) found that of 382 Twitter studies only about 5% use existing datasets collected by other researchers.”
BackgroundStudying Reddit: A Systematic Overview of Disciplines, Approaches, Methods, and Ethics
Proferes, N., Jones, N., Gilbert, S., Fiesler, C., & Zimmer, M. (2021) · Social Media + Society · doi:10.1177/20563051211019004 · Cited by 537 · see citing works
Self-citationA Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media
Chancellor, S., Birnbaum, M. L., Caine, E. D., Silenzio, V. M. B., & De Choudhury, M. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287587 · Cited by 214 · see citing works
Tampering with Twitter’s Sample API
Pfeffer, J., Mayer, K., & Morstatter, F. (2018) · EPJ Data Science · doi:10.1140/epjds/s13688-018-0178-0 · Cited by 135 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works
Data Agency Theory: A Precise Theory of Justice for AI Applications
Ajmani, L., Stapleton, L., Houtti, M., & Chancellor, S. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3630106.3658930 · Cited by 11 · see citing works
- Authors
- Zimmer, M., & Proferes, N. J.
- Affiliations
- University of Wisconsin–Milwaukee (US)
- Venue
- Aslib Journal of Information Management
- Publisher
- Emerald
- Published
- 2014-05-19
- Volume
- 66
- Issue
- 3
- Pages
- 250-261
- ISSN
- 0001-253X (ISSN-L)
- 2050-3806
- OpenAlex
- W2051800759
- Cited by
- 257
- References
- 4
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 14 · 2023: 16 · 2022: 39 · 2021: 33 · 2020: 22 · 2019: 24 · 2018: 30 · 2017: 30 · 2016: 18 · 2015: 8 · 2014: 4
- FWCI
- 125.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Aslib Journal of Information Management (publishedVersion)
- Topics
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Social Sciences › Sociology and Political Science › Focus Groups and Qualitative Methods
- Social Sciences › Sociology and Political Science › Data Analysis and Archiving
- Keywords
- Twitter data collection, research ethics, social media research, disciplinary trends, research methodology
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