Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trolling (2024)

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 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.

## introduction in online communities, trolling is characterized as a disruptive activity, such as teasing, provoking anger, offending others, dominating discussions, or manipulating opinions @xcite . such behaviors often interfere with the productive exchange of ideas @xcite , contribute to polarized and hostile atmospheres @xcite , and cause significant emotional distress to victims @xcite . to preserve a positive community atmosphere, moderation is essential, as it helps mitigate the impact of trolling and maintain the continuity of constructive discussions @xcite . however, determining the appropriate response to trolls is not straightforward. as @xcite noted, the range of trolling behaviors is diverse, and the corresponding response strategies for addressing them should vary accordingly. for example, when faced with highly politicized and offensive comments, responses should explicitly and strongly incorporate clear warnings. by contrast, when encountering off-topic opinions during focused discussions, responses should gently guide them to realign their contributions with the goals of the discussion. this range of behaviors and required responses adds to the challenge of choosing the most appropriate strategy for each specific situation. a recent study @xcite has found that humans tend to prefer certain strategies when countering hate speech. inspired by this finding, we hypothesized that humans might also have a preferred response tailored to each distinct troll situation. to investigate this, we explored whether preferences exist for various response strategies to different trolling behaviors. our findings showed a clear correlation between the types of trolling encountered and response strategies preferred, enhancing our understanding of how to counter different trolling behaviors appropriately. in this paper, we aim to develop a method for generating the most effective strategy for responding to trolls in diverse situations, thereby promoting a desirable online community environment. accordingly, we propose a method that recommends a specific response strategy for each type of trolling behavior, which enables the generation of appropriate counter-responses (cr) to trolls aligned with human preference. to this end, we investigated the relationship between different trolling strategies (ts) and the corresponding preferred response strategies (rs). then, we constructed a dataset that matches rs to user preferences across various troll contexts. utilizing this dataset, we developed a recommendation system for rs and designed a cr generation methodology that selects the most appropriate strategy based on this system. our experimental results demonstrate the gap between crs generated by general-purpose large language models (llms) and human-preferable crs, highlighting the importance of aligning human preferences with strategies for effective cr generation. our contributions and findings are threefold: ## related works trolling behaviors vary widely, from explicit expressions of hate, such as promoting discrimination based on gender, to subtle annoyance, including digressing onto irrelevant topics or misleading others with harmful advice @xcite @xcite @xcite . @xcite outlined the types of trolling strategies ranging from covert to overt and examined the types of response strategies accordingly. attempts to implement automatic countertrolling have been made @xcite @xcite @xcite , but the challenge of automatically selecting the appropriate rs still remains. in this study, we explore effective cr generation strategies to address these gaps. when moderating trolls to preserve a healthy online community environment, a critical factor is community approval of the intervention approach @xcite . common responses to trolling include ignoring @xcite , deleting comments @xcite , and banning users or communities @xcite . however, these approaches have been criticized for potential contagion of such behavior @xcite , leading to censorship accusations @xcite , and neglecting user feedback @xcite . while recent advancements in llms have led to instruction-integrated interactive moderation @xcite showing impressive response generation capabilities, there remains a need for more targeted approaches to combat trolling effectively, as @xcite found that the commonly used gentle guiding approach is not universally preferred. in this paper, we explore how to choose the appropriate rs for countering trolls, motivated by the previous research that highlights significant variations in preferences for responding to hate speech @xcite . ## methodology in this section, we explore the relationship between ts and preferred rs, detailing the process we used to construct a dataset that aligns human preferences with rs. our dataset comprises troll comments paired with crs preferred by human participants, selected from multiple crs. furthermore, we outline our method for generating crs by leveraging the distribution of rs derived from this dataset. ## experiments in this section, we conduct experiments to evaluate the effectiveness of our proposed approach for generating crs to trolls. to demonstrate the importance of aligning crs with human preferences, we compare crs produced by our model against those generated by existing models using human evaluation metrics. ## conclusion in this work, we explored the importance of aligning human preferences with response strategies to address the challenge of trolling in online communities. we constructed a dataset via human annotation that captures the relationship between types of trolling and preferred counter-responses. this dataset showed distinct differences in preferences for response strategies depending on various troll strategies. in our experiments, we leveraged this relationship to generate human-preferred and effective counter-responses. our approach not only succeeded in generating more preferred counterresponses but also promoted constructive discussions and mitigated the harmful impact of trolling. for future work, we recommend a deeper analysis of preference differences across demographics and communities to further enhance online community health and user engagement. this research paves the way for further advancements in interactive moderation, enabling more targeted and effective approaches to combat trolling.