An Interactive Framework for Profiling News Media Sources
Nikhil Mehta Dan Goldwasser
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.