A Deep Dive into Multilingual Hate Speech Classification
Integrity signals
Registry
Cites
Statistics
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?
Lack of transparency makes it difficult for humans to understand what is done behind the scenes and what causes the performance of the model to be either low, moderate, or high.
“This makes it difficult for humans to understand what is done behind the scenes and what causes the performance of the model to be either low, moderate, or high [158].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [158]
The citation lists haven’t been fetched yet
References 39 · Cited by 37
- Authors
- Aluru, S. S., Mathew, B., Saha, P., & Mukherjee, A.
- Affiliations
- Indian Institute of Technology Kharagpur (IN)
- Corresponding author
- Mathew, Binny
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 423-439
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3130335560
- Cited by
- 37
- References
- 39
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 5 · 2023: 11 · 2022: 7 · 2021: 2
- FWCI
- 2.79
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- cross-lingual classification, zero-shot classification, low-resource languages, BERT, social media, hate speech datasets
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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