Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trolling
Abstract: 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 that recommends an appropriate RS for various trolling behaviors. This approach is supported by a dataset we constructed, which aligns these strategies with user preferences. This enables the generation of effective counter-responses by recommending the most appropriate strategies based on these preferences. The experimental results demonstrate that our proposed approach improves discussion quality and reduces the negative effects of trolls, thereby enhancing the online community environment.
Paper Type: Short
Research Area: Computational Social Science and Cultural Analytics
Research Area Keywords: Trolling, Counter Response Generation, Reddit, Social media, Online communities, Moderation
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 775
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