Integrating Argumentation and Hate-Speech-based Techniques for Countering Misinformation (2024)

Abstract: The proliferation of online misinformation presents a significant challenge, requiring scalable strategies for effective mitigation. While detection methods exist, current reactive approaches, like content flagging and banning, are short-term and insufficient. Additionally, advancements like large language models (LLMs) exacerbate the issue by enabling large-scale creation and dissemination of misinformation. Thus, sustainable, scalable solutions that encourage behavior change and broaden perspectives by persuading misinformants against their viewpoints or broadening their perspectives are needed. To this end, we propose persuasive LLM-based dialogue systems to tackle misinformation. However, challenges arise due to the lack of suitable datasets and formal frameworks for generating persuasive responses. Inspired by existing methods for countering online hate speech, we explore adapting counter-hate response strategies for misinformation. Since misinformation and hate speech often coexist despite differing intentions, we develop classifiers to identify and annotate response strategies from hate-speech counter-responses for use in misinformation scenarios. Human evaluations show a 91% agreement on the applicability of these strategies to misinformation. Next, as a scalable counter-misinformation solution, we create an LLM-based argument graph framework that generates persuasive responses, using the strategies as control codes to adjust the style and content. Human evaluations and case studies demonstrate that our framework generates expert-like responses and is 14% more engaging, 21% more natural, and 18% more factual than the best available alternatives.

## introduction misinformation is the unintentional falsification of information and can be a life-threatening menace @xcite on online social media platforms. the public availability of advanced technologies such as llms, enables the rapid creation of false information at scale, and the ease of access to the web across all demographics aids its dissemination @xcite @xcite , causing an uptake in the acceptance of fake news. while most means of combating misinformation @xcite resort to myopic approaches like content flagging and banning, they do little to change the perception of the misinformant. although such reactive measures reduce the spread of false information, they are momentary, as the misinformant still holds the incorrect perception and is likely to re-share the false information again. hence, we need scaleable solutions that attempt perception change, leading to a lasting reduction in the spread of false news. as per @xcite , 96% of countermisinformation responses are by other (non-expert) social media users, which effectively curbs misinformation @xcite @xcite and reduces misperceptions @xcite @xcite @xcite across topics @xcite @xcite @xcite , platforms, and demographics @xcite . although scalable, unlike experts, most non-expert user responses are rude and use unverified evidence @xcite , propelling mistrust @xcite and further agitation @xcite @xcite . on the other hand, expert-curated responses are factually consistent, more informative, and more effective. however, the expert responses are mostly template-like and generic, making them less engaging and persuasive. furthermore, there are far fewer experts than non-experts, which makes it hard to keep up with the volume of misinformation. hence, we propose persuasive dialogue systems as a complementary solution that combines the naturalness, variety, and scalability of non-expert-based counterresponses with the informativeness and politeness of expert-based responses. however, implementing such systems requires appropriate dialogues with well-defined counter-misinformation response strategy annotations as training data, which are lacking. furthermore, although persuasion is theoretically well studied, computational frameworks are limited, which we address through our solution. online misinformation and hate speech are often interwoven in practice @xcite @xcite , where either one leads to another or is co-existent in posts. however, unlike misinformation, formal definitions of counter-hate response strategies exists @xcite @xcite . therefore, we adapt those strategies to respond to misinformation posts. we train computational models to identify counter-hate response strategies and analyze the utility of adapting such strategies to combating misinformation. we computationally annotate such strategies in diverse datasets comprising hate speech dialogues @xcite , polarized factual discussions @xcite , and misinformation @xcite , yielding a silver annotated misinformation and hatespeech dialogue corpus foot_0 . furthermore, we represent dialogue as an argument graph @xcite comprising claims and premises @xcite @xcite from the dialogue turns, and formalize persuasion as a series of decisionmaking in the graph. as a response, the generator first determines the contextual argument components to attack/support and the hate speech-based response strategy. next, it generates the response claims and premises, followed by the final response text. such a formalism fosters controllability and explainability of the generated responses. figure 1 illustrates our solution at a high level. our contributions are: computational persuasion: hunter (2018) defines computational persuasion as the study of formal models of dialogues involving arguments, counterarguments, and strategies, and outlines a framework for fostering behaviour change. meade (2021) discusses the different persuasive strategies evident from daily life, whereas cialdini and cialdini (2007) studies persuasive strategies in sales and marketing. hornsey and fielding (2017) discusses attitude roots as the underlying motivations in people and developed a "jiu jitsu" model of persuasion. ruiz dolz (2023) devised an argumentbased computational persuasive framework. al-hindi (2023) used argument structure and quality for improving fact checkers. controllable argument generation: controllable argumentation has benefited from the advancements in language modeling. keskar et al. (2019) regulated language models using control codes. hua et al. (2019); hua and wang (2018) experimented with factual counter-argument generators that can be controlled for the style. schiller et al. (2021) introduced arg-ctrl: a language model for generating sentence-level arguments using topic, stance, and aspect-based control codes. khatib et al. (2021) constructed argumentationrelated knowledge graphs and used them to control argument generation. syed et al. (2021) used control codes in extractive and abstractive models for generating conclusions from arguments. chakrabarty et al. (2021) controllably re-framed polarized arguments to reduce their fear quotient. saha and srihari (2023) used walton's argument scheme-based control codes for generating factual arguments around polarized topics. as a solution, alshomary et al. (2021) ranked argument premises by their strength and attacked the weakest premise. saha and srihari (2024) used features based on walton's argument schemes, speech acts, personality traits, and human values for controlling persuasive counter-responses. counter hate-speech and misinformation: neural approaches like countergedi (saha et al., 2022a), toxicbot (de los riscos and d'haro, 2021), and others (kiritchenko et al., 2021; windisch et al., 2021; zhu and bhat, 2021), has advanced counterhate speech generation. advancements have also been made in knowledge-based and multi-lingual models such as chung et al. (2021a) and chung et al. (2020). however, multi-turn hate speechbased dialogues are lacking. conan (chung et al., 2019), offers pairs of hate speech and counternarratives in english, french, and italian, primarily addressing islamophobia. building upon conan, multi-target conan (fanton et al., 2021) broadened the scope to include english examples covering various hate targets. moreover, chung et al. (2021b) introduced a subset of multi-conan examples grounded in knowledge. additionally, furman et al. (2022) curated responses to hateful comments from the hateval corpus (basile et al., 2019) and enhanced it with annotations derived from wagemann's periodic table of arguments (wagemans, 2016). furthermore, chasm (ashida and komachi, 2022) utilized llms to produce a syn-thetic, quality-controlled dataset for counter-hate speech, offering additional annotations conducive to counter-narrative evaluation research. lastly, bonaldi et al. (2022) introduced dialoconan, a dataset consisting of multi-turn dialogues synthetically generated and quality-controlled, depicting interactions between a hater and an ngo operator. unlike hate speech, frameworks and datasets for counter-responses to misinformation are still lacking. to address this, he et al. (2023) created misin-focorrect dataset comprising covid-19 misinformation tweets and counter-responses by experts and non-experts, and trained a factual response generator to refute false information politely. however, the dataset has a few limitations. first, it only contains misinformation posts and response pairs, making it non-conducive to lengthy conversations. second, the expert-curated responses are usually informative and generic and do not contain diverse response styles, unlike the non-experts. hence, we aim to train generators capable of informative and engaging responses conducive to dialogues. 3 adapting hate speech response strategies to misinformation counter-responses strategies to online hate speech are well studied. benesch et al. (2016) analyzed approximately 1m controversial tweets about islamophobia, lgbtq, race, refugees, women, and politics and defined a taxonomy of nine strategies of counter-responses to hate comments, detailed in figure 8 (appendix a). using benesch et al. (2016)'s strategy definitions, mathew et al. (2019) shared an annotated dataset of 7k examples (tsnh) containing counter-responses to youtube comments targetting jews, lgbt+, and people of color (poc). chung et al. (2019) released conan, a curated dataset of 6.5k hate-speech and counter-response pairs about islamophobia, and annotated the responses with benesch et al. (2016)'s response strategy definitions. consolidating the conan and tsnh examples, we train an ensemble of llm-based classifiers for identifying counterresponse strategies from given hate speech and counter-response pairs. however, since the tsnh dataset does not include the original hate comment of a counter-response, we first train an llm-based generator for synthetically generating (backfilling) hate comments from counter-responses and then train the llm-based counter-response type (cs-type) ensembled classifier on the conan and syn-thetically enriched tsnh dataset, and use it to annotate the counter-responses to misinformation posts in the misinfocorrect dataset. ## persuasive response generator here, we experiment with computational models for incorporating the hate speech-based counterresponse strategies during response generation such that changing them yields varied responses. furthermore, we experiment with models that contextually plan the response strategy before generating the response. since persuasion relies on effective argumentation (gnamus, 1986; o'keefe, 2012; rosenfeld and kraus, 2016; roque, 2017), we adopt an argumentation-based framework where we represent a dialogue as an argument graph. the generator sequentially generates a plan determining the contextual graph nodes to attack and support, determines the hate speech-based response strategy to use in the response, generates the appropriate response arguments, and finally combines the arguments as the final response text. ## conclusion the impact of counter-misinformation solutions like content flagging and banning are short-lived as the perpetrator is still misinformed. as a lasting solution, here we present an argumentation-based persuasive framework which adapts hate speechbased counter-response strategies to tackle misinformation. representing conversations as argument graphs, we implement a 2-step approach where the model first determines a response strategy and the contextual arguments to attack/support, followed by generating the response arguments and text. our mistral-7b-based persuasive responses are qualitatively deemed factual, engaging, appealing, and capable of starting a dialogue with the misinformant, which is crucial to persuasion. additionally, the 2-step approach provides stylistic control, where changing the response strategy and attack/support planning yields varied responses. also, in the process, we yield a silver annotated dataset comprising misinformation posts and response pairs with hate speech-based counter-response strategies.