An Interactive Framework for Profiling News Media Sources
This addresses the spread of misinformation on social media, which is crucial for maintaining a healthy society, though it appears incremental by integrating existing methods with human interaction.
The paper tackles the problem of detecting fake and biased news media sources by proposing an interactive framework that combines graph-based models, pre-trained large language models, and human insight, achieving rapid detection with as little as 5 human interactions even in challenging unseen settings.
The recent rise of social media has led to the spread of large amounts of fake and biased news, content published with the intent to sway beliefs. While detecting and profiling the sources that spread this news is important to maintain a healthy society, it is challenging for automated systems. In this paper, we propose an interactive framework for news media profiling. It combines the strengths of graph based news media profiling models, Pre-trained Large Language Models, and human insight to characterize the social context on social media. Experimental results show that with as little as 5 human interactions, our framework can rapidly detect fake and biased news media, even in the most challenging settings of emerging news events, where test data is unseen.