ELF22: A Context-based Counter Trolling Dataset to Combat Internet Trolls
This addresses the issue of combating internet trolls for online communities, but it is incremental as it focuses on dataset creation rather than a new method.
The authors tackled the problem of automated trolling online by creating a novel dataset for generating counter responses, which includes troll comments and labeled strategies, and demonstrated through human evaluation that models fine-tuned on this dataset significantly improve strategy-controlled sentence generation.
Online trolls increase social costs and cause psychological damage to individuals. With the proliferation of automated accounts making use of bots for trolling, it is difficult for targeted individual users to handle the situation both quantitatively and qualitatively. To address this issue, we focus on automating the method to counter trolls, as counter responses to combat trolls encourage community users to maintain ongoing discussion without compromising freedom of expression. For this purpose, we propose a novel dataset for automatic counter response generation. In particular, we constructed a pair-wise dataset that includes troll comments and counter responses with labeled response strategies, which enables models fine-tuned on our dataset to generate responses by varying counter responses according to the specified strategy. We conducted three tasks to assess the effectiveness of our dataset and evaluated the results through both automatic and human evaluation. In human evaluation, we demonstrate that the model fine-tuned on our dataset shows a significantly improved performance in strategy-controlled sentence generation.