  Social media platforms provide a rich environment for analyzing user
behavior. Recently, deep learning-based methods have been a mainstream approach
for social media analysis models involving complex patterns. However, these
methods are susceptible to biases in the training data, such as participation
inequality. Basically, a mere 1% of users generate the majority of the content
on social networking sites, while the remaining users, though engaged to
varying degrees, tend to be less active in content creation and largely silent.
These silent users consume and listen to information that is propagated on the
platform. However, their voice, attitude, and interests are not reflected in
the online content, making the decision of the current methods predisposed
towards the opinion of the active users. So models can mistake the loudest
users for the majority. We propose to leverage re-weighting techniques to make
the silent majority heard, and in turn, investigate whether the cues from these
users can improve the performance of the current models for the downstream task
of fake news detection.
