An Interactive Framework for Profiling News Media Sources

TL;DR

This paper introduces an interactive framework that combines graph models, large language models, and human input to efficiently profile and detect fake or biased news sources on social media, even during emerging events.

Contribution

It presents a novel hybrid approach integrating automated models and human insights for real-time news media profiling and fake news detection.

Findings

Rapid detection with minimal human input

Effective in emerging, unseen news scenarios

Combines multiple AI techniques for improved profiling

Abstract

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.