Performance analysis of word embeddings for cyberbullying detection
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Abstract Cyber bullying activities are increasing day by day with the increase of Social Media Platforms such as Face book, Twitter, Instagram etc. Bullies take the advantage of these large online connected platforms due to which it became as a big challenging task in Natural Language Processing (NLP). In this paper, we compare the performance of various word embedding methods from basic word embedding methods to recent advanced language models such as RoBERTa, XLNET, ALBERT, etc. for cyberbullying detection. We used LightGBM and Logistic regression classifiers for the classification of bullying and non-bullying tweets. Among all the models, RoBERTa is outperformed as compared to state-of-the-art models.
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Pericherla et al. found that the majority of language models achieved high F1 scores compared to traditional word embeddings such as BoW and TF-IDF, as well as semantic word embeddings such as word2vec, GloVe, and FastText.
“The study found that the majority of language models achieved high F1 scores compared to traditional word embeddings such as BoW and TF-IDF, as well as semantic word embeddings such as word2vec, GloVe, and FastText.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [76]
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- Authors
- Pericherla, S., & Ilavarasan, E.
- Affiliations
- Pondicherry University (IN)
- Corresponding author
- Pericherla, Subbaraju
- Venue
- IOP Conference Series: Materials Science and Engineering
- Publisher
- IOP Publishing
- Published
- 2021-02-01
- Volume
- 1085
- Issue
- 1
- Pages
- 012008
- ISSN
- 1757-8981 (ISSN-L)
- 1757-899X
- OpenAlex
- W3137460023
- Cited by
- 24
- References
- 20
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 5 · 2023: 5 · 2022: 6 · 2021: 2
- FWCI
- 2.08
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- IOP Conference Series Materials Science and Engineering (publishedVersion, cc-by)
- 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 › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- cyberbullying detection, word embeddings, RoBERTa, XLNet, ALBERT, social media text classification, tweets, LightGBM, logistic regression, language models
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