Countering Misinformation via Emotional Response Generation (2023)

Abstract: The proliferation of misinformation on social media platforms (SMPs) poses a significant danger to public health, social cohesion and ultimately democracy. Previous research has shown how social correction can be an effective way to curb misinformation, by engaging directly in a constructive dialogue with users who spread – often in good faith – misleading messages. Although professional fact-checkers are crucial to debunking viral claims, they usually do not engage in conversations on social media. Thereby, significant effort has been made to automate the use of fact-checker material in social correction; however, no previous work has tried to integrate it with the style and pragmatics that are commonly employed in social media communication. To fill this gap, we present VerMouth, the first large-scale dataset comprising roughly 12 thousand claim-response pairs (linked to debunking articles), accounting for both SMP-style and basic emotions, two factors which have a significant role in misinformation credibility and spreading. To collect this dataset we used a technique based on an author-reviewer pipeline, which efficiently combines LLMs and human annotators to obtain high-quality data. We also provide comprehensive experiments showing how models trained on our proposed dataset have significant improvements in terms of output quality and generalization capabilities.

## introduction social media platforms (smp) represent one of the most effective mediums for spreading misleading content @xcite . social media users interact with potentially false claims on a daily basis and contribute (whether intentionally or not) to their spreading. several techniques are commonly employed to construct false but convincing content: mimicking reliable media posts, as in the case of so-called "fake news"; impersonating trustworthy public figures; leveraging emotional language ( @xcite . among the different countermeasures adopted, one of the most employed is fact-checking, i.e. the task of assessing a claim's veracity. although the work of professional fact-checkers is crucial for countering misinformation @xcite , it has been shown that most debunking on smp is carried out by ordinary users through direct replies to misleading messages @xcite . in the literature, this phenomenon is called social correction @xcite . in order to keep up with the massive amount of fake news constantly being produced, natural language processing techniques have been proposed as a viable solution for the automation of fact-checking pipelines @xcite . researchers have focused on both the automatic prediction of the truthfulness of a statement (a classification task, often called veracity prediction) and the generation of a written rationale (a generation task called verdict production; @xcite . while generating a rationale is more challenging than stating a claim veracity, previous research has proven that it is more persuasive @xcite . thus, automating the verdict generation process has been deemed crucial @xcite as an aid for both fact-checkers and for social media users @xcite . an effective explanation (verdict) is characterised as being accessible (i.e. adopting a language directly and easily comprehensible by the reader) and by containing a limited number of arguments to avoid the so-called overkill backfire effect @xcite . in this paper, we contribute to automated factchecking by introducing vermouth, foot_0 the first large-scale and general-domain smp-style dataset grounded in trustworthy fact-checking articles, comprising ~12 thousand examples for the generation of personalised explanations. vermouth was collected via an efficient and effective data augmentation pipeline which combines instructionbased large language models (llms) and human post-editing. starting from harvested journalistic-style claimverdict pairs, we ran two data collection sessions: first, we focused on claims by rewriting them in a general smp-style and then adding emotional/personalisation aspects to better mimic content which can be found online. then, in the second session, the verdicts were rewritten according to pre-defined criteria (e.g. displaying empathy) to match the new claims obtained in the first session. this process is summarised in figure 1 . finally, we tested the capabilities and robustness of generative models fine-tuned over vermouth: automatic and human evaluation, as well as qualitative analysis of the generated verdicts, suggest that for social media claims, verdicts generated through models trained on vermouth are widely preferred and that those models are more robust to the changing of claim style. our analyses show that generated verdicts are deemed less effective if they are (i) either too long and filled with a high number of arguments or (ii) if they are excessively empathetic. generally, despite these limitations, our results show that verdicts written in a social and emotional style hold greater sway and effectiveness when dealing with claims presented in an smp-style. ## related work the fact-checking process is comprised of two main tasks: first, given a news story, the truthfulness/veracity of a statement has to be determined; then, an explanation (verdict) has to be produced. in the literature, the problem of determining a claim's veracity, has been framed as a binary @xcite @xcite or multi-label @xcite classification task, and occasionally addressed under a multi-task learning paradigm @xcite . given the supervised nature of these methodologies, significant efforts have been directed towards the development of datasets for evidence-based veracity prediction, such as fever @xcite , scifact @xcite , covid-fact @xcite , and politihop @xcite . for the more challenging task of verdict production, several methodologies have been explored, ranging from logic-based approaches @xcite to deep learning techniques @xcite @xcite . more recently, @xcite introduced a reinforcement learning-based framework which generates counter-misinformation responses, rewarding the generator to enhance its politeness, credibility, and refutation attitude while maintaining text fluency and relevancy. previous works have shown how casting this problem as a summarization task -starting from a claim and a corresponding fact-checking article -appears to be the most promising approach @xcite . under such framing, the explanations are either extracted from the relevant portions of manually written fact-checking articles @xcite or generated exnovo @xcite ; these two approaches correspond, respectively, to extractive and abstractive summarization. finally, @xcite proposed a hybrid approach for the generation of explanation, by employing both extractive and abstractive approaches combined into a unique pipeline. extractive and abstractive approaches suffer from known limitations: on the one hand, extrac-tive summarization cannot provide sufficiently contextualised explanations; on the other, abstractive alternatives can be prone to hallucinations undermining the justification's faithfulness. nonetheless, while the abstractive approach remains the most promising -also in light of the current advances in llms development -the problem of collecting an adequate amount of training examples persists: the few datasets available for explanation production are limited in size, domain coverage or quality. the most commonly used datasets are either machine-generated, e.g. e-fever by stammbach and ash (2020), or silver data as for liar-plus by @xcite . to the best of our knowledge, only three datasets include gold explanations, i.e. pubhealth by @xcite , the misinfocorrect's crowdsourced dataset by @xcite , and fullfact by @xcite . however, pubhealth and misinfocorrect datasets are domain-specific (respectively, health and covid-19), and only the latter comprises textual data written in an smps style (informal, personal, and empathetic if required), even if limited in size (591 entries). this style is very different from a journalistic style, more direct and concise, meant for the general public. other datasets, based on community-oriented fact-checking derived from birdwatch foot_1 @xcite , do not fit well our scenario, as users' corrections were proven to be often driven by political partisanship @xcite . ## dataset in this work, we introduce vermouth, a new largescale dataset for the generation of explanations for misinformation countering that are anchored to fact-checking articles. to build this dataset we adapted the author-reviewer pipeline presented by @xcite , wherein a large language model (the author component) produces novel data while humans (the reviewer) filter and eventually post-edit them (figure 1 ). differently from their approach, based on gpt-2, we used an instructionbased llm that does not require fine-tuning and applied it to the source data taken from a popular fact-checking website. we leveraged the authorreviewer pipeline for a style transfer task, so to generate new data in an smp-style rather than in a journalistic one. each entry in our dataset includes a triplet comprising: a claim (i.e. the factual statement under analysis), a fact-checking article (i.e. a document containing all the evidence needed to fact-check a claim), and a verdict (i.e. a short textual response to the claim which explains why it might be true or false). both the claims and the verdicts were rewritten according to the desired style using the authorreviewer pipeline. still, given the different nature and purpose of claims and verdicts, we instructed the llms with different specific requirements during two different sessions of data collection. for the first session, we further considered two phases. the goal of the first phase was to obtain claims with a generic "smp-style", i.e. something that resembles a post which can be found online, rather than the more journalistic and neutral style. in the second phase, we add an emotional component to the llm's instruction. we considered paul ekman's six basic emotions: anger, disgust, fear, happiness, sadness, and surprise @xcite . verdicts were generated in a second session as responses to each newly generated claim, using the same author-reviewer pipeline but different instructions for the llm and different guidelines for the reviewer. this was done to account for the characteristics a verdict should have, e.g. politeness, attacking the arguments and not the person, and empathy @xcite . in ## experimental design inspired by the summarization approaches proposed by @xcite ; @xcite , for the automatic generation of personalised verdicts we leveraged an lm pretrained with a summarization objective. to overcome the limitation of the model's fixed input size, we reduced the length of the input articles, by adding an extractive summarization step beforehand (following the best configuration presented in @xcite . this extractive-abstractive pipeline was tested on different configurations, both in indomain and cross-domain settings. 9 the quality of the generated verdicts was assessed with both an automatic and a human evaluation. we present and discuss the results in section 5. ## results we assessed the potential of our proposed dataset in terms of generation capabilities via both automatic and human evaluation. ## conclusion producing a verdict, i.e. a factual explanation for a claim's veracity and doing so in a constructive and engaging manner is a very demanding task. on social media platforms, this is usually done by ordinary users, rather than professional fact-checkers. in this context, automated fact-checking can be very beneficial. still, to fine-tune and/or evaluate nlg models, high-quality datasets are needed. to address the lack of large-scale and general-domain smp-style resources (grounded in trustworthy factchecking articles) we created vermouth, a novel dataset for the automatic generation of personalised explanations. the provided resource is built upon debunking articles from a popular fact-checking website, whose style has been altered via a collaborative human-machine strategy to fit realistic scenarios such as social-media interactions and to account for emotional factors.