Consolidating Strategies for Countering Hate Speech Using Persuasive Dialogues (2023)

Abstract: Hateful comments are prevalent on social media platforms. Although tools for automatically detecting, flagging, and blocking such false, offensive, and harmful content online have lately matured, such reactive and brute force methods alone provide short-term and superficial remedies while the perpetrators persist. With the public availability of large language models which can generate articulate synthetic and engaging content at scale, there are concerns about the rapid growth of dissemination of such malicious content on the web. There is now a need to focus on deeper, long-term solutions that involve engaging with the human perpetrator behind the source of the content to change their viewpoint or at least bring down the rhetoric using persuasive means. To do that, we propose defining and experimenting with controllable strategies for generating counterarguments to hateful comments in online conversations. We experiment with controlling response generation using features based on (i) argument structure and reasoning-based Walton argument schemes, (ii) counter-argument speech acts, and (iii) human characteristicsbased qualities such as Big-5 personality traits and human values. Using automatic and human evaluations, we determine the best combination of features that generate fluent, argumentative, and logically sound arguments for countering hate. We further share the developed computational models for automatically annotating text with such features, and a silver-standard annotated version of an existing hate speech dialog corpora.

## introduction hateful comments are prevalent on social media platforms @xcite and of-ten go unchecked. while most available solutions to tackling online hate resort to brute force methods such as content flagging and blocking, by not addressing the underlying cause, such methods usually provide superficial and temporary respite. they often yield contradictory outcomes by further agitating the perpetrator, thus fueling hate, and even pose as restraints to online free speech, which leads to demonstrations. the steep growth of online hate speech is difficult for human regulators to keep up with, which is further aggravated by public access to large language models (llms) @xcite that are capable of generating fluent yet synthetic content at scale. hence, there is a need for deep and scalable means for amicably addressing hate speech. instead of only being reactive, a solution should aim at changing the perception of the initiator of hate speech. it should try to minimize the rhetoric by either persuading the perpetrator of hate speech contrary to their standpoint or at the least trying to broaden their perception and making them aware of the other facets of the topic of discussion. online conversational agents can be such a robust solution to countering online hate. persuasive conversations that strive to change the mental state of an interlocutor rely on subtler human-based appeals. according to the ancient greek philosopher aristotle, persuading an audience depends on how well a person appeals in three areas: logos, ethos, and pathos @xcite . recent research has engendered generalpurpose llms like chatgpt @xcite , gpt-4, bard @xcite , and inter-alia, whose fluent conversational capabilities have left the public in awe. although impressive, without being explicitly trained to persuade, their efficacy in the purposeful and nuanced human task of persuasion is questionable. hence, there is a need to develop controllable conversational systems that can incorporate such appeals while generating persuasive counter-hate responses. here we attempt to consolidate and establish a set of features from diverse sources that can aid control and guide computational models to construct counter-arguments to hateful comments in online conversations. we broadly identify two feature categories for capturing the different aspects of a response: (i) valuebased: features based on human qualities and characteristics such as big-5 personality traits @xcite and human values @xcite . (ii) structure-based: features based on the patterns of reasoning such as walton argument schemes @xcite and argumentative discourse-based speech acts @xcite such as denouncing, questioning, etc. we incorporate a two-step process where first, we automatically annotate available hate speech dialogue datasets with the value and structure-based features using computational models. second, using the annotated features as guides, we experiment with computational models for generating counter-arguments to hate speech. we experiment with different feature combinations to determine the best features for guiding counter-argument generation and report automatic and human evaluation results. in the process, we yield a silver-standard annotated version of the hate speech dialog corpora, with additional computational models for automatically annotating text with the defined features. we summarize our contributions as follows: please note that this paper is strictly linguistically motivated and does not compose any psychographic or demographically motivated data profiling, analysis, or segmentation. the proposed methods only utilize diverse sources to derive features based on single turns of short text around specific topics, with the motivation being identifying observable stylometric differences between hateful and counter-hate response text. we use the features as control codes @xcite for aiding language modeling for counter-argument generation and hypothesize that such control codes can better partition the learned probability distribution. furthermore, since the datasets used in the experiments are created synthetically by experts, the derived textual features do not portray the actual traits of users in a live setting. through our experiments, we only highlight the differences in the presence of features, and the actual labels do not matter for our purpose. ## related work there has been noticeable advancement in gearing technology to curb the menace of online hate @xcite . @xcite proposed a three-module pipeline approach where they generate multiple counterarguments, and filters using a bert-based model @xcite , and select the most relevant response using a novel retrieval-based method. saha et al. (2022a) implemented countergedi, an ensemble approach to enable dialogpt (zhang et al., 2019) generate polite, detoxified, and emotionally laden counterspeech. de los riscos and d'haro (2021) introduced toxicbot, a system that detects and generates responses to intervene during online conversations with hate speech content. (chung et al., 2021b) introduced an external knowledgebased counter-narrative generation pipeline that can generate factual arguments across multiple domains. chung et al. (2020) leveraged geppetto for effectively generating counter-narratives in italian. most available counter-hate speech corpora are limited to single-turn pairs of hate speech and counter-hate response, while very few focus on multi-turn dialogues. tekiroglu et al. (2020) presented a study on using gpt-2 (radford et al., 2019) for generating responses to hate and produced a silver-standard annotated dataset. chung et al. (2019) introduced conan, which contains hate speech/counter-narrative pairs in english, french, and italian about islamophobia. using a human-inthe-loop approach, fanton et al. (2021) released multi-target conan. they expanded conan to include english examples across multiple hate targets. chung et al. (2021c) released a knowledgegrounded subset of multi-target conan examples. furman et al. (2022) collected responses to the hateful comments in the hateval corpus (basile et al., 2019) and further enriched it with annotations based on wagemanns periodic table of argu-ments @xcite . @xcite employed llms to create chasm, a synthetic and quality-controlled counter-hate dataset. they provided further annotations that can also facilitate research in counter-narrative evaluation. @xcite shared dialoconan, a multiturn synthetically generated and quality-controlled dialogue dataset between a hater and an ngo operator. we use dialoconan for our experiments and incorporate diverse features as control codes @xcite to better guide counterargument generation. ## defining and annotating features as described in ## response generation we experiment with controllable computational models for generating responses in hateful dialogues. our objectives are: (i) assessing the feasibility and utility of using the value and structurebased features for generating and controlling the counter-arguments to hateful speech. (ii) determining the best feature combination for optimal control over response generation. (iii) understanding the importance of each of the four sets of features. ## conclusion motivated to minimize online hate by persuading perpetrators of hate speech contrary to their beliefs, we experiment with ways to generate controllable counter-hate arguments using features derived from psychology, philosophy, linguistics, and social science. we experiment with different feature combinations for generating fluent and persuasive counterarguments. we share computational models for automatically annotating text with such features and a silver-standard annotated hate speech dialog corpora. our evaluations indicate that a convincing argument should generally appeal to the hateful perpetrator's personality while incorporating appropriate patterns of reasoning.