This paper presents a study on COVID-19 vaccine misinformation in three middle-income countries: Brazil, Indonesia, and Nigeria. It introduces a multilingual dataset of such misinformation, consisting of annotated tweets. The…
Continue readingThe paper presents an approach to detecting violence-inciting texts on social media, a problem that has been exacerbated in low-resource settings due to sparse research and less data. The authors…
Continue readingThis study investigates how people consume health videos on social media. The research involved interviews and surveys, leading to a model that characterizes users’ video consumption practices. These practices include…
Continue readingThis article discusses a study aimed at protecting user-side privacy in online self-disclosure. The researchers developed a taxonomy of 19 self-disclosure categories and used a language model for identification, achieving…
Continue readingThis article presents a study conducted in two rounds with individuals diagnosed with binge eating disorder and bulimia. The study aimed to understand their motivations and practices when consuming digital…
Continue readingThe study introduces MIDDAG, a system that visualizes how COVID-19 news travels on social media. It provides insights into user and community susceptibility levels and popular opinions raised while spreading…
Continue readingThe study investigates user responses to misinformation flags in a hypothetical social media setting. It focuses on whether these warnings influence users’ perceived accuracy judgement and sharing intent of the…
Continue readingThis study presents MentalLLaMA, the first open-source Large Language Model (LLM) designed for interpretable mental health analysis on social media. Traditional methods for automatic mental health analysis on social media…
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