A survey of learning-based techniques of email spam filtering
Open accessIntegrity signals
Registry
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References 96 · Cited by 407
- Authors
- Blanzieri, E., & Bryl, A.
- Affiliations
- University of Trento (IT)
- Corresponding author
- Bryl, Anton
- ORCID
- Blanzieri, Enrico
- Venue
- Artificial Intelligence Review
- Published
- 2008-03
- Volume
- 29
- Issue
- 1
- Pages
- 63-92
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W2015585187
- Cited by
- 407
- References
- 96
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 12 · 2023: 14 · 2022: 24 · 2021: 27 · 2020: 26 · 2019: 28 · 2018: 33 · 2017: 34 · 2016: 24 · 2015: 35 · 2014: 30 · 2013: 23 · 2012: 28
- FWCI
- 53.81
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Artificial Intelligence Review (publishedVersion)
- Unitn Eprints Research (Università Degli Studi di Trento) (acceptedVersion)
- Iris (University of Trento) (submittedVersion, other-oa)
- Iris (University of Trento) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- email spam detection, machine learning, anti-spam techniques
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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.
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