Decoding the Silent Majority: Inducing Belief Augmented Social Graph
  with Large Language Model for Response Forecasting
Chenkai Sun Jinning Li Yi R. Fung Hou Pong Chan Tarek Abdelzaher ChengXiang Zhai Heng Ji
Automatic response forecasting for news media plays a crucial role in
enabling content producers to efficiently predict the impact of news releases
and prevent unexpected negative outcomes such as social conflict and moral
injury. To effectively forecast responses, it is essential to develop measures
that leverage the social dynamics and contextual information surrounding
individuals, especially in cases where explicit profiles or historical actions
of the users are limited (referred to as lurkers). As shown in a previous
study, 97% of all tweets are produced by only the most active 25% of users.
However, existing approaches have limited exploration of how to best process
and utilize these important features. To address this gap, we propose a novel
framework, named SocialSense, that leverages a large language model to induce a
belief-centered graph on top of an existent social network, along with
graph-based propagation to capture social dynamics. We hypothesize that the
induced graph that bridges the gap between distant users who share similar
beliefs allows the model to effectively capture the response patterns. Our
method surpasses existing state-of-the-art in experimental evaluations for both
zero-shot and supervised settings, demonstrating its effectiveness in response
forecasting. Moreover, the analysis reveals the framework's capability to
effectively handle unseen user and lurker scenarios, further highlighting its
robustness and practical applicability.