A Fine-Grained Taxonomy of Replies to Hate Speech (2023)

Abstract: Countering rather than censoring hate speech has emerged as a promising strategy to address hatred. There are many types of counterspeech in user-generated content: addressing the hateful content or its author, generic requests, well-reasoned counter arguments, insults, etc. The effectiveness of counterspeech, which we define as subsequent incivility, depends on these types. In this paper, we present a theoretically grounded taxonomy of replies to hate speech and a new corpus. We work with real, user-generated hate speech and all the replies it elicits rather than replies generated by a third party. Our analyses provide insights into the content real users reply with as well as which replies are empirically most effective. We also experiment with models to characterize the replies to hate speech, thereby opening the door to estimating whether a reply to hate speech will result in further incivility.

## introduction counterspeech refers to a "direct response that counters hate speech" @xcite . it is a remedy to address hate speech @xcite by spelling out the hateful content conveyed in an utterance, or challenging, questioning, rejecting, disputing or confronting the hate @xcite . distinct from approaches to hate mitigation by content moderation @xcite , counterspeech is preferable as it does not interfere with the principle of free and open public spaces for debate @xcite . recently, the nlp communities have contributed corpora for the detection @xcite @xcite and categorization @xcite of counterspeech. categorizing hateful post: you are full of fat f**k liberals who jump to say a person died because they didn't wear a flimsy piece of cloth and not because they made awful life decisions and stuffed their faces. counterspeech addressing the author of hate speech can lead to completely different conversational outcomes than counterspeech addressing hateful content. in this paper, we present a categorization of counterspeech that is well-grounded on theories in argument mining and builds a bridge between these two related yet unconnected research areas. specifically, our categorization differentiates between counterspeech that addresses the author of hate speech and the hateful content itself. therefore, it could provide useful insights into counterspeech generation and evaluation strategies. importantly, our categorization can help uncover how language usage in different types of replies is tied to the future conversation trajectory. the main contributions are as follows: hate speech there have been several studies on hate speech, including efforts on defining taxonomies for hate speech. examples include hate speech against both individuals and groups @xcite , against some specific identities @xcite , against only a single identity @xcite , or categories for implicit hate @xcite . counterspeech is a strategy to address hate speech that does not require content removal, and it has received increasing attention. although a few works have built fine-grained counterspeech taxonomies @xcite @xcite , they do not differentiate between the target of the counterspeech (i.e., the author of hate or the hate itself). in addition, they fail to convey the intensity of hostile tone in counterspeech @xcite . for example, insulting somebody's intelligence is more uncivil than asking them to leave the discussion. there are a few synthetic datasets that have been curated with the help of trained operators @xcite @xcite or by generative models @xcite . however, synthetic counterspeech is not as rich as genuine counterspeech and rarely used by real users. for example, "[. . . ] are inappropriate" and "[. . . ] should be avoided" made up 5.6% of replies in the synthetic counterspeech dataset by @xcite . on the other hand, we found no real users using this kind of generic language to counter hate speech in our dataset. in this paper, we work with counterspeech written by regular people out of their own motivations and desires. counterspeech vs counterargument a counterargument is considered an ad-hominem argument if it argues against the author rather than the content @xcite . otherwise, it argues against the content of the argument by denying the premises, conclusion, or the reasoning between them @xcite . there are two main differences between counterspeech and counterarguments. first, counterspeech does not necessarily attack a hateful comment-it may provide suggestions to the author of the hateful comment (e.g., "please be nice to others"). on the other hand, a counterargument always attacks either an argument or its author. second, when addressing the content, a counterspeech comment does not require supporting evidence, while a counterargument usually does @xcite . ## a taxonomy of replies to hate speech we present a new taxonomy to categorize replies to hate speech that comprises four primary categories and eight secondary categories. it bridges the gap between argumentation theories @xcite @xcite and counterspeech encountered in genuine user-generated web data. the taxonomy covers two dimensions of arguments: logos (i.e., logical arguments) and pathos (i.e., appealing to emotion) @xcite . therefore, our categories are not mutually exclusive but represent principal types of replies to hate speech. by detailing argument components, this taxonomy facilitates (a) the evaluation of logical fallacies and emotional appeals in counterspeech and (b) the analysis of how they relate to ineffective and uncivil communication. figure 1 shows the taxonomic structure. ## data collection and annotation we choose reddit as the source of data and use the pushshift api to retrieve whole conversation threads. 2 to mitigate topic and author biases that keyword sampling may introduce @xcite , we use communitybased sampling. we select 35 subreddits that are thought to be hateful @xcite @xcite , including subreddits such as r/mensrights, r/seduction, and r/purplepilldebate. we refer the reader to appendix a for the full list. there are a total of 1,382,596 comments from 5,325 submissions. ## corpus analysis ## experiments we experiment with models to solve two tasks: if it is counter author, counter content, noncounter agree, or non-counter neutral; 2. counterspeech sub-type classification. given a reply that disagrees with a hateful comment (i.e., counter author or counter content), these models determine the sub-type of counterspeech from our ontology. we use a 70-15-15 split for each task. all of our models are neural classifiers with the roberta transformer @xcite as the main component. we use the pretrained models by hugging-face @xcite and pytorch @xcite to implement our models.