Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language (2023)

Abstract: Counterspeech, i.e., responses to counteract potential harms of hateful speech, has become an increasingly popular solution to address online hate speech without censorship. However, properly countering hateful language requires countering and dispelling the underlying inaccurate stereotypes implied by such language. In this work, we draw from psychology and philosophy literature to craft six psychologically inspired strategies to challenge the underlying stereotypical implications of hateful language. We first examine the convincingness of each of these strategies through a user study, and then compare their usages in both human- and machine-generated counterspeech datasets. Our results show that human-written counterspeech uses countering strategies that are more specific to the implied stereotype (e.g., counter examples to the stereotype, external factors about the stereotype’s origins), whereas machine-generated counterspeech uses less specific strategies (e.g., generally denouncing the hatefulness of speech). Furthermore, machine generated counterspeech often employs strategies that humans deem less convincing compared to human-produced counterspeech. Our findings point to the importance of accounting for the underlying stereotypical implications of speech when generating counterspeech and for better machine reasoning about anti-stereotypical examples.

## introduction counterspeech, i.e., responses that counteract hateful or dangerous content @xcite , has emerged as a widely supported solution to address hateful online speech without censorship risks of deletion-based content moderation @xcite . however, as the scale of hateful content increases online @xcite , effectively responding with counterspeech is infeasible for humans to do alone. as ## background: language, stereotypes, and counterspeech our goal is to investigate stereotype-targeting counterspeech strategies that most convincingly challenge the underlying implications of hate speech. our analyses and strategies are based on the fact that prejudiced language about social groups can often convey and exacerbate stereotypes through subtle implicature @xcite @xcite . properly combating such language requires convincingly dispelling and fact-checking the stereotypical implications beyond simple deflection or norm-setting (lepoutre, 2019, §2.2). additionally, our investigations aim to shed light on how nlp systems can help humans better analyze and produce counterspeech ( §2.3). ## stereotype-targeting strategies to investigate how counterspeech can address stereotypical speech, we examine stereotypes through the lens of generics about social groups @xcite , i.e., short unquantified statements that link a social group with a quality: for example, the stereotype in through this formulation, we introduce six countering strategies that aim to challenge the acceptability, validity, or truthfulness of stereotypes ex-pressed as generics, crafted based on prior work on combating stereotypes and essentialism in social psychology and finding exceptions to generics in philosophy of language. alternate groups (alt-grp) people often accept generics of the form 'g relation q' as true based on relatively weak evidence @xcite . however, for unfamiliar groups, we often attribute qualities more assertively, claiming that (almost) 'all g relation q' even with scarce supporting evidence @xcite . to counter such inferences, we can remind readers that alternate groups' relation to q is often higher than commonly recognized. we define the alt-grp strategy as follows: here, alt(g) represents a relevant set of alternative groups to g with the same set of separating characteristic (e.g., gender). for instance, if @xmath0, alt(g) would consist of a group like "men" rather than a set of unrelated groups. alternate qualities (alt-qual) similarly, we construct a strategy that highlights alternate distinctive or defining qualities of g. we consider values of alt(q) to promote a more nuanced understanding and challenge preconceived notions about the group. thus, we formulate the alt-qual strategy as the following: in this case, alt(q) represents alternate contextually relevant qualities. for example, if @xmath1., alt(q) would include qualities like "intelligent" and "profound" but not positive and unrelated quality (e.g., fun). this aims to combat the definitional aspect of a stereotype. counterexamples (cntr-exs) counterexamples aim to point out exceptions to the stereotype and thereby challenge the implication that the stereotype holds for all members of the group. foot_0 ## human evaluation of stereotype-targeting strategies in order to determine which strategies are persuasive in countering implied stereotypical beliefs, we first conduct a user study on amazon mechanical turk (mturk) to measure the convincingness of expert-crafted counter-statements, i.e., short sentences that embody a specific countering strategy. ## usage and convincingness results in this section, we discuss the results of our investigations regarding the usage and persuasive power of our six stereotype-targeting strategies, for both human and machine-written counterspeech. ## conclusion & discussion in this work, we investigated stereotype-targeting strategies that convincingly counter the underlying implication of harmful speech. we crafted six different strategies ( §3) and observed human preferences under a controlled setting ( §4) and in two datasets ( §5). we compared the strategy usage in persuasive vs. unpersuasive human generated counterspeech and in human vs. machine-generated counterspeech ( §6). humans prefer more specific strategies. in both our user study ( §4) and data study ( §5), alt-qual was the most preferred and used method. similarly, the delta award difference and the preference ranking both showed external, broad, and cntr-exs as more convincing. these top strategies, with the exception of broadening, are methods that require specific reasoning about the implication. moreover, correctly identifying external factors and devise counter examples requires up-to-date world knowledge. in this work, we did not focus on the correctness of the usage of these strategies by machines, but -given their persuasiveness to humans -further study is required to ensure their quality.