Quantitative Study of Mis/Disinformation

Session Submission Type: Created Panel

Session Description

The panel features applications of AI and machine learning techniques to address the challenges posed by misinformation and disinformation on social media. The papers emphasize the development and implementation of automated tools and models, like large language models, locality sensitive hashing, and NLP techniques, to detect, analyze, and counteract false information efficiently. These approaches aim to enhance the timely and accurate identification of problematic content, track the diffusion of narratives globally, and assess the impact and spread of misinformation, especially in politically sensitive contexts.

Sub Unit

• Division 8: Political Methodology

Individual Presentations

• Automating the Detection of Mis/Disinformation on Social Media Platforms - Clifton van der Linden, McMaster University; Deena Abul-Fottouh, Dalhousie University; John R. McAndrews, McMaster University; Mickael Temporao, Université Laval; Corentin Vande Kerckhove, Université catholique de Louvain; Victor Kuperman, McMaster University; Hugo Mailhot; Colin Timmers

• Misinformation Detection with Generative AI - Kellin Pelrine, McGill University; Jean-Francois Godbout, Universite de Montreal; Reihaneh Rabbany, McGill University

• Overcoming the AI “Black Box” in Political Analysis: A Hybrid Approach - Suso B Baleato, Universidade de Santiago de Compostela; Erik Marino, University of Évora; Renata Vieira, University of Évora

• Protest Event Analysis in Autocracies: The Use of NLP to Collect Protest Data - Bogdan Mamaev, Griffith University; Jean Dinco

Chair

• Joshua A. Tucker New York University

Discussant

• Emily Thorson Syracuse University