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The False positive problem of automatic bot detection in social science research
Open accessAbstract
The identification of bots is an important and complicated task. The bot classifier "Botometer" was successfully introduced as a way to estimate the number of bots in a given list of accounts and, as a consequence, has been frequently used in academic publications. Given its relevance for academic research and our understanding of the presence of automated accounts in any given Twitter discourse, we are interested in Botometer's diagnostic ability over time. To do so, we collected the Botometer scores for five datasets (three verified as bots, two verified as human; n = 4,134) in two languages (English/German) over three months. We show that the Botometer scores are imprecise when it comes to estimating bots; especially in a different language. We further show in an analysis of Botometer scores over time that Botometer's thresholds, even when used very conservatively, are prone to variance, which, in turn, will lead to false negatives (i.e., bots being classified as humans) and false positives (i.e., humans being classified as bots). This has immediate consequences for academic research as most studies in social science using the tool will unknowingly count a high number of human users as bots and vice versa. We conclude our study with a discussion about how computational social scientists should evaluate machine learning systems that are developed for identifying bots.
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“They also demonstrate that Botometer's limits, even when applied very conservatively, are subject to fluctuation, which can result in both false positives and false negatives (i.e., the classification of humans as bots) and false negatives (i.e., the classification of bots as humans).”
From Deepfakes: current and future trends (2024) · cites this paper as Rauchfleisch and Kaiser 2020
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“Their findings demonstrate that Botometer ratings are inaccurate for estimating bots, particularly when used in an alternate language.”
From Deepfakes: current and future trends (2024) · cites this paper as Rauchfleisch and Kaiser 2020
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Metadata
- Authors
- Rauchfleisch, A., & Kaiser, J.
- Affiliations
- National Taiwan University (TW)
- Harvard University (US)
- Corresponding author
- Rauchfleisch, Adrian
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-10-22
- Volume
- 15
- Issue
- 10
- Pages
- e0241045
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC7580919
- OpenAlex
- W3014935876
- Cited by
- 184
- References
- 49
- Citations by year
- 2026: 10 · 2025: 23 · 2024: 39 · 2023: 31 · 2022: 47 · 2021: 27 · 2020: 7
- FWCI
- 26.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Science and Technology, Taiwan (108-2410-H-002 -007 -MY2)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- bot detection, Botometer, false positives, false-negative results, Twitter, language influence, computational social science
- MeSH terms
- Machine Learning, Humans, Politics, Research, Social Sciences, Social Networking, Social Media
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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