Exploring the Impact of Lexicon-based Knowledge Transfer for Hate Speech Detection in Indonesia Code-Mixed Languages
Authors: 
Endang Wahyu Pamungkas, Dian Purworini, Diah Priyawati, Rona Rizkhy Bunga ChasanaAuthors Info & Claims
Endang Wahyu Pamungkas, Dian Purworini, Diah Priyawati, Rona Rizkhy Bunga ChasanaAuthors Info & ClaimsPages 85 - 90
Abstract
In this study, our objective is to examine the influence of external knowledge from a lexicon on knowledge transfer for mitigating the language shift issue in the detection of hate speech in code-mixed languages. To accomplish this, we constructed a lexicon based on findings from previous studies. Subsequently, we implemented several machine learning models with various scenarios to assess the impact of the lexicon. The experimental results demonstrate that incorporating lexicon features leads to improved performance in detecting hate speech within code-mixed languages. Particularly, utilizing a lexicon that encompasses both implicit and explicit lexicons yields the most favorable outcomes in this investigation. This research provides valuable insights into understanding the detection of hate speech in code-mixed Indonesian languages and contributes to the advancement of more robust systems aimed at fostering a safer and more inclusive online environment. By leveraging the lexicon and exploring the interplay between implicit and explicit elements in hate speech, this study enhances our understanding of addressing hate speech challenges in Indonesia code-mixed languages and paves the way for developing more effective detection mechanisms.
References
[1]
Ajeng Dwi Asti, Indra Budi, and Muhammad Okky Ibrohim. 2021. Multi-label Classification for Hate Speech and Abusive Language in Indonesian-Local Languages. In 2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE, 1–6.
[2]
Elisa Bassignana, Valerio Basile, Viviana Patti, 2018. Hurtlex: A multilingual lexicon of words to hurt. In CEUR Workshop proceedings, Vol. 2253. CEUR-WS, 1–6.
[3]
Irina Bigoulaeva, Viktor Hangya, Iryna Gurevych, and Alexander Fraser. 2023. Label modification and bootstrapping for zero-shot cross-lingual hate speech detection. Language Resources and Evaluation (2023), 1–32.
[4]
Bharathi Raja Chakravarthi, Ruba Priyadharshini, Shubanker Banerjee, Manoj Balaji Jagadeeshan, Prasanna Kumar Kumaresan, Rahul Ponnusamy, Sean Benhur, and John Philip McCrae. 2023. Detecting abusive comments at a fine-grained level in a low-resource language. Natural Language Processing Journal 3 (2023), 100006.
[5]
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Doha, Qatar, 1724–1734. https://doi.org/10.3115/v1/D14-1179
[6]
Abhishek Chopra, Deepak Kumar Sharma, Aashna Jha, and Uttam Ghosh. 2023. A Framework for Online Hate Speech Detection on Code-mixed Hindi-English Text and Hindi Text in Devanagari. ACM Transactions on Asian and Low-Resource Language Information Processing 22, 5 (2023), 1–21.
[7]
Gretel Liz De la Peña Sarracén and Paolo Rosso. 2023. Systematic keyword and bias analyses in hate speech detection. Information Processing & Management 60, 5 (2023), 103433. https://doi.org/10.1016/j.ipm.2023.103433
[8]
Suman Dowlagar and Radhika Mamidi. 2022. Hate speech detection on code-mixed dataset using a fusion of custom and pre-trained models with profanity vector augmentation. SN Computer Science 3, 4 (2022), 306.
[9]
Vishwajeet Dwivedy and Pradeep Kumar Roy. 2023. Deep feature fusion for hate speech detection: a transfer learning approach. Multimedia Tools and Applications (2023), 1–23.
[10]
HateBase. 2023. HateBase. https://hatebase.org/
[11]
Mardhiya Hayaty, Sumarni Adi, and Anggit D Hartanto. 2020. Lexicon-based indonesian local language abusive words dictionary to detect hate speech in social media. Journal of Information Systems Engineering and Business Intelligence 6, 1 (2020), 9–17.
[12]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Comput. 9, 8 (Nov. 1997), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
[13]
Md Saroar Jahan and Mourad Oussalah. 2023. A systematic review of hate speech automatic detection using natural language processing. Neurocomputing 546 (2023), 126232. https://doi.org/10.1016/j.neucom.2023.126232
[14]
Hiren Madhu, Shrey Satapara, Sandip Modha, Thomas Mandl, and Prasenjit Majumder. 2023. Detecting offensive speech in conversational code-mixed dialogue on social media: A contextual dataset and benchmark experiments. Expert Systems with Applications 215 (2023), 119342.
[15]
Roberto Navigli and Simone Paolo Ponzetto. 2012. BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial intelligence 193 (2012), 217–250.
[16]
Endang Wahyu Pamungkas, Valerio Basile, and Viviana Patti. 2021. A joint learning approach with knowledge injection for zero-shot cross-lingual hate speech detection. Information Processing & Management 58, 4 (2021), 102544.
[17]
Endang Wahyu Pamungkas, Azizah Fatmawati, Yusuf Sulistyo Nugroho, Dedi Gunawan, and Endah Sudarmilah. 2022. Hate Speech Detection in Code-Mixed Indonesian Social Media: Exploiting Multilingual Languages Resources. In 2022 Seventh International Conference on Informatics and Computing (ICIC). IEEE, 1–5.
[18]
Endang Wahyu Pamungkas, Azizah Fatmawati, and Farah Danisha Salam. 2022. Hate Speech Detection on Indonesian Social Media: A Preliminary Study on Code-Mixed Language Issue. In Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval. 104–109.
[19]
Endang Wahyu Pamungkas and Viviana Patti. 2019. Cross-domain and cross-lingual abusive language detection: A hybrid approach with deep learning and a multilingual lexicon. In Proceedings of the 57th annual meeting of the association for computational linguistics: Student research workshop. 363–370.
[20]
Ishaani Priyadarshini, Sandipan Sahu, and Raghvendra Kumar. 2023. A transfer learning approach for detecting offensive and hate speech on social media platforms. Multimedia Tools and Applications (2023), 1–27.
[21]
Shofianina Dwi Ananda Putri, Muhammad Okky Ibrohim, and Indra Budi. 2021. Abusive language and hate speech detection for Javanese and Sundanese languages in tweets: Dataset and preliminary study. In 2021 11th International Workshop on Computer Science and Engineering, WCSE 2021. International Workshop on Computer Science and Engineering (WCSE), 461–465.
[22]
Michael Wiegand, Josef Ruppenhofer, Anna Schmidt, and Clayton Greenberg. 2018. Inducing a lexicon of abusive words–a feature-based approach. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 1046–1056.
Index Terms
- Exploring the Impact of Lexicon-based Knowledge Transfer for Hate Speech Detection in Indonesia Code-Mixed Languages
Recommendations
A cross-lingual transfer learning method for online COVID-19-related hate speech detection
AbstractDuring the COVID-19 pandemic, online social media platforms such as Twitter facilitate the exchange of information among people. However, the prevalence of “infodemic” such as online hate speech has exacerbated social rifts, discrimination, ...
Highlights- Suggested a cross-lingual COVID-19-related hate speech detection problem.
- Introduced cross-lingual contrastive learning.
- Validated model performance on datasets in three languages.
Hate Speech Detection on Code-Mixed Dataset Using a Fusion of Custom and Pre-trained Models with Profanity Vector Augmentation
AbstractWith the increase in user-generated content on social media networks, hate speech and offensive language content are also increasing. From the perspective of computer science, automatic detection of such hate speech and offensive language content ...
Hate Speech Detection in Roman Urdu
Special issue on Deep Learning for Low-Resource Natural Language Processing, Part 1 and Regular PapersHate speech is a specific type of controversial content that is widely legislated as a crime that must be identified and blocked. However, due to the sheer volume and velocity of the Twitter data stream, hate speech detection cannot be performed ...
Comments
Information & Contributors
Information
Published In
December 2023
336 pages
ISBN:9798400709227
DOI:10.1145/3639233
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 March 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
NLPIR 2023
NLPIR 2023: 2023 7th International Conference on Natural Language Processing and Information Retrieval
December 15 - 17, 2023
Seoul, Republic of Korea
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 9Total Downloads
- Downloads (Last 12 months)9
- Downloads (Last 6 weeks)1
Reflects downloads up to 27 Jul 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format