A panel discussion hosted by MBZUAI in collaboration with the Manara Center for Coexistence and Dialogue addressed misinformation and its threat to elections. The talk covered the reasons behind the rise of misinformation, citizen perspectives, and the role of social media influencers. Two cases, the Indian general elections of 2024 and the upcoming US presidential elections in November 2024, were used to describe the contours of misinformation. Why it matters: Understanding the dynamics of misinformation, especially through social media influencers, is crucial for safeguarding democratic processes in the region and globally.
Iryna Gurevych from TU Darmstadt discussed challenges in using NLP for misinformation detection, highlighting the gap between current fact-checking research and real-world scenarios. Her team is working on detecting emerging misinformation topics and has constructed two corpora for fact checking using larger evidence documents. They are also collaborating with cognitive scientists to detect and respond to vaccine hesitancy using effective communication strategies. Why it matters: Addressing misinformation is crucial in the Middle East, especially regarding public health and socio-political issues, making advancements in NLP-based fact-checking highly relevant.
MBZUAI Professor Preslav Nakov is researching methods to identify and combat the harmful uses of large language models in generating disinformation. He notes that disinformation, unlike fake news, is weaponized with the intent to persuade, not just to lie. His research focuses on the linguistic differences between human-written and machine-generated disinformation, such as the use of rhetorical devices in human propaganda. Why it matters: As AI-generated content becomes more prevalent, understanding and mitigating its potential for spreading disinformation is critical for maintaining trust and integrity in information ecosystems, especially during major election cycles.
Researchers from MBZUAI and other institutions presented a study at ACL 2024 on combatting misinformation by identifying misrepresented scientific research. They compiled a dataset called MISSCI, comprised of real-world examples of misinformation gathered from the HealthFeedback fact-checking website. The annotators classified the different types of errors in reasoning into nine different classes. Why it matters: This work addresses a critical need to combat the spread of scientific falsehoods online, especially given the challenges of manual fact-checking.
MBZUAI Professor Preslav Nakov is researching methods to combat fake news and online disinformation through NLP techniques. His work focuses on detecting harmful memes and identifying the stance of individuals regarding disinformation. Four of Nakov’s recent papers on these topics were presented at NAACL 2022. Why it matters: This research aims to mitigate the impact of weaponized news and online manipulation, contributing to a more trustworthy information environment in the region and globally.
A new paper from MBZUAI researchers explores using ChatGPT to combat the spread of fake news. The researchers, including Preslav Nakov and Liangming Pan, demonstrate that ChatGPT can be used to fact-check published information. Their paper, "Fact-Checking Complex Claims with Program-Guided Reasoning," was accepted at ACL 2023. Why it matters: This research highlights the potential of large language models to address the growing challenge of misinformation, with implications for maintaining information integrity in the digital age.
Iryna Gurevych from TU Darmstadt presented research on using large language models for real-world fact-checking, focusing on dismantling misleading narratives from misinterpreted scientific publications and detecting misinformation via visual content. The research aims to explain why a false claim was believed, why it is false, and why the alternative is correct. Why it matters: Addressing misinformation, especially when supported by seemingly credible sources, is critical for public health, conflict resolution, and maintaining trust in institutions in the Middle East and globally.
This paper provides an overview of the UrduFake@FIRE2021 shared task, which focused on fake news detection in the Urdu language. The task involved binary classification of news articles into real or fake categories using a dataset of 1300 training and 300 testing articles across five domains. 34 teams registered, with 18 submitting results and 11 providing technical reports detailing various approaches from BoW to Transformer models, with the best system achieving an F1-macro score of 0.679.