Twitter spammer detection using data stream clustering
Integrity signals
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Among the 126 features used by Miller et al. [31], 95 are based on the textual content of tweets.
“Among the 126 features, 95 are based on the textual content of tweets.”
From The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race (2017) · cites this paper as [31]
The unsupervised approach of Miller et al. [31] achieved the worst performances among all techniques benchmarked in this study.
“As shown in Table 7, this achieved the worst performances among all those that we have benchmarked in this study.”
From The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race (2017) · cites this paper as [31]
The citation lists haven’t been fetched yet
References 27 · Cited by 332
- Authors
- Miller, Z., Dickinson, B., Deitrick, W., Hu, W., & Wang, A. H.
- Affiliations
- Houghton University (US)
- Pennsylvania State University (US)
- Corresponding author
- Hu, Wei
- ORCID
- Hu, Wei
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2014-03
- Volume
- 260
- Pages
- 64-73
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2020754036
- Cited by
- 332
- References
- 27
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 32 · 2023: 22 · 2022: 29 · 2021: 26 · 2020: 43 · 2019: 47 · 2018: 30 · 2017: 26 · 2016: 23 · 2015: 20 · 2014: 11
- FWCI
- 52.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Sciences (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- Twitter spam detection, data stream clustering, anomaly detection, hashtags, mentions, spammer detection
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.
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