BREE-HD: A Transformer-Based Model to Identify Threats on Twitter
With the world transitioning to an online reality and a surge in social media users, detecting online harassment and threats has become more pressing than ever. Gendered cyber-hate causes women significant social, psychological, reputational, economic, and political harm. To tackle this problem, we...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10168907/ |
| _version_ | 1797783599412412416 |
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| author | Sinchana Kumbale Smriti Singh G. Poornalatha Sanjay Singh |
| author_facet | Sinchana Kumbale Smriti Singh G. Poornalatha Sanjay Singh |
| author_sort | Sinchana Kumbale |
| collection | DOAJ |
| description | With the world transitioning to an online reality and a surge in social media users, detecting online harassment and threats has become more pressing than ever. Gendered cyber-hate causes women significant social, psychological, reputational, economic, and political harm. To tackle this problem, we develop a dataset and propose a transformer-based model to classify tweets into threats or non-threats that are either sexist or non-sexist. We have developed a model to identify sexist and non-sexist threats from a collection of sexist, non-sexist tweets. BREE-HD performs extraordinarily well with an accuracy of 97% when trained on the dataset we developed to detect threats from a collection of derogatory tweets. To provide insight into how BREE-HD makes classifications, we apply explainable A.I. (XAI) concepts to provide a detailed qualitative analysis of our proposed methodology. As an extension of our work, BREE-HD could be used as a part of a system that could detect threats targeting people specifically tailored to classify them in real-time adequately. |
| first_indexed | 2024-03-13T00:29:07Z |
| format | Article |
| id | doaj.art-83e77dd9f0c741a1a1b924e63de503df |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T00:29:07Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-83e77dd9f0c741a1a1b924e63de503df2023-07-10T23:00:18ZengIEEEIEEE Access2169-35362023-01-0111671806719010.1109/ACCESS.2023.329107210168907BREE-HD: A Transformer-Based Model to Identify Threats on TwitterSinchana Kumbale0https://orcid.org/0009-0003-8989-1769Smriti Singh1https://orcid.org/0009-0001-6479-3308G. Poornalatha2Sanjay Singh3https://orcid.org/0000-0001-7212-5919Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Computer Science, The University of Texas at Austin, Austin, TX, USADepartment of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaWith the world transitioning to an online reality and a surge in social media users, detecting online harassment and threats has become more pressing than ever. Gendered cyber-hate causes women significant social, psychological, reputational, economic, and political harm. To tackle this problem, we develop a dataset and propose a transformer-based model to classify tweets into threats or non-threats that are either sexist or non-sexist. We have developed a model to identify sexist and non-sexist threats from a collection of sexist, non-sexist tweets. BREE-HD performs extraordinarily well with an accuracy of 97% when trained on the dataset we developed to detect threats from a collection of derogatory tweets. To provide insight into how BREE-HD makes classifications, we apply explainable A.I. (XAI) concepts to provide a detailed qualitative analysis of our proposed methodology. As an extension of our work, BREE-HD could be used as a part of a system that could detect threats targeting people specifically tailored to classify them in real-time adequately.https://ieeexplore.ieee.org/document/10168907/Explainable AIhate speech detectionsexism detectionthreat detectiontransformers |
| spellingShingle | Sinchana Kumbale Smriti Singh G. Poornalatha Sanjay Singh BREE-HD: A Transformer-Based Model to Identify Threats on Twitter IEEE Access Explainable AI hate speech detection sexism detection threat detection transformers |
| title | BREE-HD: A Transformer-Based Model to Identify Threats on Twitter |
| title_full | BREE-HD: A Transformer-Based Model to Identify Threats on Twitter |
| title_fullStr | BREE-HD: A Transformer-Based Model to Identify Threats on Twitter |
| title_full_unstemmed | BREE-HD: A Transformer-Based Model to Identify Threats on Twitter |
| title_short | BREE-HD: A Transformer-Based Model to Identify Threats on Twitter |
| title_sort | bree hd a transformer based model to identify threats on twitter |
| topic | Explainable AI hate speech detection sexism detection threat detection transformers |
| url | https://ieeexplore.ieee.org/document/10168907/ |
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