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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.10417 |
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| _version_ | 1866917753727746048 |
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| author | Tassava, Matthew Kolodjski, Cameron Milbrath, Jordan Bishop, Adorah Flanders, Nathan Fetsch, Robbie Hanson, Danielle Straub, Jeremy |
| author_facet | Tassava, Matthew Kolodjski, Cameron Milbrath, Jordan Bishop, Adorah Flanders, Nathan Fetsch, Robbie Hanson, Danielle Straub, Jeremy |
| contents | This paper presents and evaluates work on the development of an artificial intelligence (AI) anti-bullying system. The system is designed to identify coordinated bullying attacks via social media and other mechanisms, characterize them and propose remediation and response activities to them. In particular, a large language model (LLM) is used to populate an enhanced expert system-based network model of a bullying attack. This facilitates analysis and remediation activity - such as generating report messages to social media companies - determination. The system is described and the efficacy of the LLM for populating the model is analyzed herein. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10417 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Development of an AI Anti-Bullying System Using Large Language Model Key Topic Detection Tassava, Matthew Kolodjski, Cameron Milbrath, Jordan Bishop, Adorah Flanders, Nathan Fetsch, Robbie Hanson, Danielle Straub, Jeremy Artificial Intelligence Computation and Language This paper presents and evaluates work on the development of an artificial intelligence (AI) anti-bullying system. The system is designed to identify coordinated bullying attacks via social media and other mechanisms, characterize them and propose remediation and response activities to them. In particular, a large language model (LLM) is used to populate an enhanced expert system-based network model of a bullying attack. This facilitates analysis and remediation activity - such as generating report messages to social media companies - determination. The system is described and the efficacy of the LLM for populating the model is analyzed herein. |
| title | Development of an AI Anti-Bullying System Using Large Language Model Key Topic Detection |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2408.10417 |