Changing Perspectives: Is It Sufficient to Detect Social Bots?
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Botometer's generality and ease of deployment are counterbalanced by reduced bot detection accuracy.
“The generality and ease of deployment of this detector are however counterbalanced by a reduced bot detection accuracy [5, 17].”
From A decade of social bot detection (2020) · cites this paper as [17]
Initial efforts towards adversarial bot detection were driven by the creativity of some researchers and only covered few cases with limited applicability.
“The previous analysis highlights that initial efforts towards adversarial bot detection were driven by the creativity of some researchers and only covered few cases with limited applicability [9, 17].”
From A decade of social bot detection (2020) · cites this paper as [17]
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- Authors
- Grimme, C., Assenmacher, D., & Adam, L.
- Affiliations
- University of Münster (DE)
- Corresponding author
- Grimme, Christian
- Assenmacher, Dennis
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 445-461
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2805358007
- Cited by
- 93
- References
- 17
- Citations by year
- 2025: 9 · 2024: 15 · 2023: 11 · 2022: 11 · 2021: 10 · 2020: 31 · 2019: 6
- FWCI
- 9.22
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- social bot detection, automated accounts, Twitter, hybrid detection, public opinion manipulation, strategic attack
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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