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Detection of Hate Speech Text in Hindi-English Code-mixed Data
Open accessAbstract
Social media sites like Twitter, Facebook, being user-friendly and a free source, provide opportunities to people to air their voice. People, irrespective of the age group, use these sites to share every moment of their life making these sites flooded with data. Apart from these commendable features, these sites have down side as well. Due to lack of restrictions set by these sites for its users to express their views as they like, anybody can make adverse and unrealistic comments in abusive language against anybody with an ulterior motive to tarnish one’s image and status in the society. So it became a huge responsibility for the Government and these sites to identify this hate content before it disseminates to mass. Automatic hate speech detection faces quite a lot of challenges due to the non-standard variations in spelling and grammar. Especially for a country like India with huge multilingual and bilingual population, this hate content would be in code-mixed form which makes the task demanding. So our paper projects a machine learning model to detect hate speech in Hindi-English code-mixed social media text. The methodology makes use of Facebook’s pre-trained word embedding library, fastText to represent 10000 data samples collected from different sources as hate and non-hate. The performance of the proposed methodology is compared with word2vec and doc2vec features and it is observed that fastText features gave better feature representation with Support Vector Machine (SVM)-Radial Basis Funcrion (RBF) classifier. The paper also provides an insight to the researchers working in the field of code-mixed data that character level features provide best result for code-mixed data.
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“Long Short-Term Memory Networks with Attention (LSTM-Att): By fusing the capacity of LSTMs to capture long-term dependencies in sequential data with the interpretability of attention mechanisms, the model has demonstrated promising results in the detection of cyberbullying.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [120]
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“Therefore, LSTM-Att might be a powerful tool for enhancing the precision of automated systems to detect cyberbullies [120].”
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A Dataset of Hindi-English Code-Mixed Social Media Text for Hate Speech Detection
Bohra, A., Vijay, D., Singh, V. K., Akhtar, S. S., & Shrivastava, M. (2018) · doi:10.18653/v1/w18-1105 · Cited by 272
Machine Learning with SVM and other Kernel methods
Soman, K. P., Loganathan, R., & Ajay (2009) · Cited by 238
Self-citationDETECTION OF HATE SPEECH IN SOCIAL NETWORKS: A SURVEY ON MULTILINGUAL CORPUS
Alhassan, A., & Al-Dossari, H. Z. (2019) · doi:10.5121/csit.2019.90208 · Cited by 188
"I am borrowing ya mixing ?" An Analysis of English-Hindi Code Mixing in Facebook
Bali, K., Sharma, J., Choudhury, M., & Vyas, Y. (2014) · Workshop on Computational Approaches to Code Switching · doi:10.3115/v1/w14-3914 · Cited by 183
Cyberhate: A review and content analysis of intervention strategies
Blaya, C. (2018) · Aggression and Violent Behavior · doi:10.1016/j.avb.2018.05.006 · Cited by 159
Did you offend me? Classification of Offensive Tweets in Hinglish Language
Mathur, P., Sawhney, R., Ayyar, M. P., & Shah, R. R. (2018) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w18-5118 · Cited by 126
Research works printed but not linked: 3
- Biere, Shanita, Sandjai Bhulai, and Master Business Analytics. (2018) "Hate Speech Detection Using Natural Language Processing Techniques". Diss. Master’s dissertation, Vrije Universiteit Amsterdam.
- Santos, Igor, Nadia Nedjah, and Luiza de Macedo Mourelle. (2017) "Sentiment analysis using convolutional neural network with fastText embeddings." 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI):1-5 · DOI
- Remmiya Devi, G" P.V. Veena, M. Anand Kumar, and K.P. Soman. (2016) "AMRITA-CEN® FIRE 2016: Code-mix entity extraction for Hindi-English and Tamil-English tweets." CEUR Workshop Proceedings:304-308.
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Metadata
- Authors
- Sreelakshmi, K., Premjith, B., & Soman, K. P.
- Affiliations
- Amrita Vishwa Vidyapeetham (IN)
- Corresponding author
- Sreelakshmi, K
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 171
- Pages
- 737-744
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W3032985518
- Cited by
- 152
- References
- 16
- Citations by year
- 2026: 15 · 2025: 30 · 2024: 25 · 2023: 40 · 2022: 21 · 2021: 18 · 2020: 3
- FWCI
- 7.82
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- 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 › Text and Document Classification Technologies
- Keywords
- hate speech detection, Hindi-English code-mixing, social media text, FastText embeddings, word embeddings, character-level features, support vector machine, Gaussian kernel, multilingual text classification, abusive language
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