The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Tweets as impact indicators: Examining the implications of automated “bot” accounts on Twitter
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” [30].”
From Measuring scientific impact beyond academia: An assessment of existing impact metrics and proposed improvements (2017) · cites this paper as [30]
Citations
Cites
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Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature
Haustein, S., Peters, I., Sugimoto, C. R., Thelwall, M. A., & Larivière, V. (2013) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23101 · Cited by 434
Self-citationSocial media use in the research workflow
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Tweets vs. Mendeley readers: How do these two social media metrics differ?
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Detecting and Analyzing Automated Activity on Twitter
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The role of Twitter in the life cycle of a scientific publicationPreprint
Darling, E. S., Shiffman, D. S., Côté, I. M., & Drew, J. (2013) · doi:10.7287/peerj.preprints.16 · Cited by 58
Altmetrics: Too soon for use in assessment
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Measuring Twitter activity of arXiv e-prints and published papers
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Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Mowbray M., 2010, Proceedings of the Sixth International Conference on Web Information Systems and Technologies, WEBIST (2), 299
Cited by
Sorted by citations
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Varol, O., Ferrara, E., Davis, C., Menczer, F., & Flammini, A. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14871 · Cited by 981
“Many social bots perform useful functions, such as dissemination of news and publications (Lokot and Diakopoulos 2016; Haustein et al. 2016) and coordination of volunteer activities (Savage, Monroy-Hernandez, and Höllerer”
BackgroundScholarly use of social media and altmetrics: A review of the literature
Sugimoto, C. R., Work, S., Larivière, V., & Haustein, S. (2017) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23833 · Cited by 680
Self-citationArming the public with artificial intelligence to counter social bots
Yang, K., Varol, O., Davis, C. A., Ferrara, E., Flammini, A., & Menczer, F. (2019) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.115 · Cited by 419
“This threshold approach is useful for scientists counting bots in a collection (Su´arez-Serrato et al., 2016; Haustein et al., 2016; Gorwa, 2017; Stella et al., 2018; Shao et al., 2018a), but it is not intuitive for humans evaluating a single account.”
BackgroundSocial Media, Echo Chambers, and Political Polarization
Barberá, P. (2020) · Social Media and Democracy · doi:10.1017/9781108890960.004 · Cited by 373
An Investigation of Brand-Related User-Generated Content on Twitter
Liu, X., Burns, A. C., & Hou, Y. (2017) · Journal of Advertising · doi:10.1080/00913367.2017.1297273 · Cited by 266
Metadata
- 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 › Library and Information Sciences › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
- Social Sciences › Library and Information Sciences › Academic Publishing and Open Access
- Keywords
- Twitter bots, altmetrics, research impact metrics, research evaluation, cyborg, scholarly communication, tweet engagement
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