Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting

• URL: http://arxiv.org/abs/2310.13297v1
• Date: Fri, 20 Oct 2023 06:17:02 GMT
• Title: Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting
• Authors: Chenkai Sun, Jinning Li, Yi R. Fung, Hou Pong Chan, Tarek Abdelzaher, ChengXiang Zhai, Heng Ji
• Abstract summary: SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
• Score: 74.68371461260946
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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