E x U : AI Models for Examining Multilingual Disinformation Narratives and Understanding their Spread (2024)

Abstract: Addressing online disinformation requires analysing narratives across languages to help fact-checkers and journalists sift through large amounts of data. The ExU project focuses on developing AI-based models for multilingual disinformation analysis, addressing the tasks of rumour stance classification and claim retrieval. We describe the ExU project proposal and summarise the results of a user requirements survey regarding the design of tools to support fact-checking.

## progress we conducted a survey of user requirements for our proposed tools at the voices festival of journalism and media literacy, foot_0 which brings together journalists, fact-checkers, researchers, and educators. participants were recruited among the visitors to the emif (european media and information fund) booth. the survey consisted of 24 questions covering basic demographic information, exposure to multiple languages, and features of stance classification and claim retrieval. we obtained 29 survey responses. almost all of participants (97%) encountered content in multiple languages when performing fact-checks. figure 1 indicates the counts of the languages from the participant's answers. 3 for a fact-checking tool in general, participants would like content translated into a language of their choice, so it will be necessary to ensure accuracy of translations both between our target languages and into other userspecified languages. for stance detection, respondents deemed it most important to automatically predict the stance of the post regarding the target claim and to automatically highlight the main argument of posts. for claim retrieval, respondents would like a high-level summary of the claim's fact-checks in addition to the fact-checks themselves. ## future work based on the initial survey results, we aim for our stance classification models to output accurate and explainable predictions for content across the target languages. towards this we will utilise multilingual transformers such as aya @xcite , which covers all the languages in figure 1 . to address the lack of data for low-resource languages we may obtain small amounts of target language fine-tuning data, as previous work found this improved results @xcite . additionally, we may translate low-resource languages into english before performing classification to leverage the knowledge from english models. we are exploring explainability via feature attribution and rationale extraction, and our preliminary research shows promise for using extractive rationales in multiple languages. still, explanations ought to be consistent across languages and invariant to translation, yet previous work showed a performance gap in explainability methods between mono-and multi-lingual models @xcite , so we plan to explore this in depth. for multilingual claim retrieval, we aim to employ a retrieval augmented generation model @xcite to help end users efficiently discern factual claims from debunked ones. this model may facilitate the existing tools for extraction of textual claims from any textual content found online and match them with existing fact-checks contained in our multiclaim dataset @xcite . the dataset contains 293,169 fact-checked articles and their corresponding claims in 39 languages. the output of the model will include the list of fact-checked claims relevant for each textual claim from the analysed textual content, their language and source references, along with the central claim summarisation of the retrieved claims in natural language.