StopHC: A Harmful Content Detection and Mitigation Architecture for Social Media Platforms
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866912113059954688 |
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| author | Truică, Ciprian-Octavian Constantinescu, Ana-Teodora Apostol, Elena-Simona |
| author_facet | Truică, Ciprian-Octavian Constantinescu, Ana-Teodora Apostol, Elena-Simona |
| contents | The mental health of social media users has started more and more to be put at risk by harmful, hateful, and offensive content. In this paper, we propose \textsc{StopHC}, a harmful content detection and mitigation architecture for social media platforms. Our aim with \textsc{StopHC} is to create more secure online environments. Our solution contains two modules, one that employs deep neural network architecture for harmful content detection, and one that uses a network immunization algorithm to block toxic nodes and stop the spread of harmful content. The efficacy of our solution is demonstrated by experiments conducted on two real-world datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06138 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | StopHC: A Harmful Content Detection and Mitigation Architecture for Social Media Platforms Truică, Ciprian-Octavian Constantinescu, Ana-Teodora Apostol, Elena-Simona Social and Information Networks Computation and Language The mental health of social media users has started more and more to be put at risk by harmful, hateful, and offensive content. In this paper, we propose \textsc{StopHC}, a harmful content detection and mitigation architecture for social media platforms. Our aim with \textsc{StopHC} is to create more secure online environments. Our solution contains two modules, one that employs deep neural network architecture for harmful content detection, and one that uses a network immunization algorithm to block toxic nodes and stop the spread of harmful content. The efficacy of our solution is demonstrated by experiments conducted on two real-world datasets. |
| title | StopHC: A Harmful Content Detection and Mitigation Architecture for Social Media Platforms |
| topic | Social and Information Networks Computation and Language |
| url | https://arxiv.org/abs/2411.06138 |