Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trolling
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910634893901824 |
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| author | Lee, Huije Song, Hoyun Shin, Jisu Cho, Sukmin Han, SeungYoon Park, Jong C. |
| author_facet | Lee, Huije Song, Hoyun Shin, Jisu Cho, Sukmin Han, SeungYoon Park, Jong C. |
| contents | Trolling in online communities typically involves disruptive behaviors such as provoking anger and manipulating discussions, leading to a polarized atmosphere and emotional distress. Robust moderation is essential for mitigating these negative impacts and maintaining a healthy and constructive community atmosphere. However, effectively addressing trolls is difficult because their behaviors vary widely and require different response strategies (RSs) to counter them. This diversity makes it challenging to choose an appropriate RS for each specific situation. To address this challenge, our research investigates whether humans have preferred strategies tailored to different types of trolling behaviors. Our findings reveal a correlation between the types of trolling encountered and the preferred RS. In this paper, we introduce a methodology for generating counter-responses to trolls by recommending appropriate RSs, supported by a dataset aligning these strategies with human preferences across various troll contexts. The experimental results demonstrate that our proposed approach guides constructive discussion and reduces the negative effects of trolls, thereby enhancing the online community environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04164 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trolling Lee, Huije Song, Hoyun Shin, Jisu Cho, Sukmin Han, SeungYoon Park, Jong C. Computation and Language Trolling in online communities typically involves disruptive behaviors such as provoking anger and manipulating discussions, leading to a polarized atmosphere and emotional distress. Robust moderation is essential for mitigating these negative impacts and maintaining a healthy and constructive community atmosphere. However, effectively addressing trolls is difficult because their behaviors vary widely and require different response strategies (RSs) to counter them. This diversity makes it challenging to choose an appropriate RS for each specific situation. To address this challenge, our research investigates whether humans have preferred strategies tailored to different types of trolling behaviors. Our findings reveal a correlation between the types of trolling encountered and the preferred RS. In this paper, we introduce a methodology for generating counter-responses to trolls by recommending appropriate RSs, supported by a dataset aligning these strategies with human preferences across various troll contexts. The experimental results demonstrate that our proposed approach guides constructive discussion and reduces the negative effects of trolls, thereby enhancing the online community environment. |
| title | Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trolling |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.04164 |