Tweets as impact indicators: Examining the implications of automated “bot” accounts on T witter
Open accessAbstract
This brief communication presents preliminary findings on automated T witter accounts distributing links to scientific articles deposited on the preprint repository ar X iv. It discusses the implication of the presence of such bots from the perspective of social media metrics (altmetrics), where mentions of scholarly documents on T witter have been suggested as a means of measuring impact that is both broader and timelier than citations. Our results show that automated T witter accounts create a considerable amount of tweets to scientific articles and that they behave differently than common social bots, which has critical implications for the use of raw tweet counts in research evaluation and assessment. We discuss some definitions of T witter cyborgs and bots in scholarly communication and propose distinguishing between different levels of engagement—that is, differentiating between tweeting only bibliographic information to discussing or commenting on the content of a scientific work.
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Haustein et al. (2014) suggest that papers are more likely to be tweeted if they are "curious or funny, have potential health applications or refer to a catastrophe".
“Haustein et al. (2014) suggest that papers are more likely to be tweeted if they are “curious or funny, have potential health applications or refer to a catastrophe” [30].”
From Measuring scientific impact beyond academia: An assessment of existing impact metrics and proposed improvements (2017) · cites this paper as [30]
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Arroyo‐Machado, W., Torres‐Salinas, D., & Robinson‐García, N. (2021) · Scientometrics · doi:10.1007/s11192-021-04167-8 · Cited by 24 · see citing works
Zhen, L., Yan, B., Tang, J. L., Nan, Y., & Yang, A. (2022) · The Information Society · doi:10.1080/01972243.2022.2139031 · Cited by 24 · see citing works
Zhang, L., Gou, Z., Fang, Z., Sivertsen, G., & Huang, Y. (2023) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24830 · Cited by 23 · see citing works
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Altmetric Prevalence in the Social Sciences, Arts and Humanities: Where are the Online Discussions?
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Comparing the Utility of Different Classification Schemes for Emotive Language Analysis
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Conclusion: The Challenges and Opportunities for Social Media Research
Persily, N., & Tucker, J. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108890960.014 · Cited by 23 · see citing works
Bot identification: Helping analysts for right data in twitter
Velayutham, T., & Tiwari, P. K. (2017) · International Conference on Advances in Computing, Communication & Automation (ICACCA) · doi:10.1109/icaccaf.2017.8344722 · Cited by 22 · see citing works
Co‐saved, co‐tweeted, and co‐cited networks
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Mapping social media attention in Microbiology: identifying main topics and actors
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Societal Impact Measurement of Research Papers
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Tweet Coupling: a social media methodology for clustering scientific publications
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ResearchGate Score, full-text research items, and full-text reads: a follow-up study
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- Authors
- Haustein, S., Bowman, T. D., Holmberg, K., Tsou, A., Sugimoto, C. R., & Larivière, V.
- Affiliations
- Université de Montréal (CA)
- University of Turku (FI)
- Indiana University Bloomington (US)
- Université du Québec à Montréal (CA)
- Corresponding author
- Haustein, Stefanie
- Bowman, Timothy D.
- Holmberg, Kim
- Tsou, Andrew
- Sugimoto, Cassidy R.
- Larivière, Vincent
- ORCID
- Venue
- Journal of the Association for Information Science and Technology
- Publisher
- Wiley
- Published
- 2015-05-05
- Volume
- 67
- Issue
- 1
- Pages
- 232-238
- ISSN
- 2330-1635 (ISSN-L)
- 2330-1643
- OpenAlex
- W1953503995
- Cited by
- 231
- References
- 25
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 10 · 2023: 9 · 2022: 25 · 2021: 17 · 2020: 43 · 2019: 37 · 2018: 30 · 2017: 27 · 2016: 11 · 2015: 4
- FWCI
- 22.20
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Journal of the Association for Information Science and Technology (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Papyrus : Institutional Repository (Université de Montréal) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Alfred P. Sloan Foundation (G-2014-3-25)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Keywords
- Twitter bots, altmetrics, research impact metrics, research evaluation, cyborg, scholarly communication, tweet engagement
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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