  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.
