Review of Recent Trends in the Detection of Hate Speech and Offensive Language on Social Media
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Abstract In the article, we describe recent trends in the detection of hate speech and offensive language on social media. We accord from the latest studies and scientific contributions. The article describes current trends and the most used methods in connection with the detection of hate speech and offensive language. At the same time, we focus on the importance of emoticons, hashtags, and swearing in the field of social networks. We point out the topicality of the selected topic, describe the next direction of our work, and suggest possible solutions to current problems in this field of research.
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Several studies used DistilBERT for cyberbullying detection in social networks and obtained promising performance.
“Several studies [54-56] used DistilBERT for cyberbullying detection in social networks. Their experimental results show that they obtained a promising performance while using DistilBERT as the word-embedding technique as well as the fine-tuned classifier.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [55]
The Text Prediction section reports promising DistilBERT results in studies of offensive-language detection, including YouTube comments and relatively high F1 scores. It does not identify those studies as cyberbullying detection or establish that DistilBERT was used both as a word-embedding technique and as a fine-tuned classifier. Page numbers are unavailable in our copy. (LLM, conf=0.85)
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Cites
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Sorted by citations
A Review on Deep-Learning-Based Cyberbullying Detection
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Self-citation
- Authors
- Sokolová, Z., Staš, J., & Juhár, J.
- Affiliations
- Technical University of Košice (SK)
- Venue
- Acta Electrotechnica et Informatica
- Publisher
- Walter de Gruyter GmbH
- Published
- 2022-12-01
- Volume
- 22
- Issue
- 4
- Pages
- 18-24
- ISSN
- 1335-8243 (ISSN-L)
- 1338-3957
- OpenAlex
- W4389495513
- Cited by
- 6
- References
- 11
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 1
- FWCI
- 0.49
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Acta Electrotechnica et Informatica (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Ministerstvo školstva, vedy, výskumu a športu Slovenskej republiky (2/0165/21)
- Agentúra na Podporu Výskumu a Vývoja (APVV-15-0517, SK-TW-21-0002, APVV-15)
- Kultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR (009TUKE-4-2019, KEGA 009TUKE-4-2019)
- Vedecká Grantová Agentúra MŠVVaŠ SR a SAV (VEGA 2/0165/21, VEGA 1/0753/20, 1/0753/20, 2/0165/21)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- hate speech detection, offensive language detection, social media, emoticons, hashtags, swearing, online abuse
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