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.

自动响应预测对新闻媒体具有重要作用，能够帮助内容制作者高效预测新闻发布的影响，防止意外的负面结果，如社会冲突和道德伤害。为了有效地预测响应，必须开发利用社交动态和上下文信息的度量，特别是在用户的明确个人资料或历史行为有限的情况下（被称为潜伏者）。本研究提出了一种新颖的框架，名为 SocialSense，它利用大型语言模型在现有社交网络之上诱导出基于信念的图，并通过基于图的传播捕捉社交动态。我们假设搭建跨越远程用户之间的连接、共享相似信念的诱导图能够有效捕捉响应模式。我们的方法在零样本和监督设置的实验评估中超过了现有的最新技术，证明了它在响应预测方面的有效性。此外，分析揭示了该框架有效处理未见用户和潜伏者情况的能力，进一步突出了它的稳健性和实用性。