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.

翻译：新闻媒体的自动响应预测在帮助内容创作者高效预判新闻发布影响、预防社会冲突和道德伤害等负面结果方面至关重要。为有效预测响应，必须开发能够利用个体周围社交动态与情境信息的度量方法，尤其在用户显式画像或历史行为数据有限（即“潜水用户”）的情况下。先前研究表明，仅25%的最活跃用户贡献了97%的推文。然而，现有方法在如何最佳处理并利用这些关键特征方面探索有限。为填补这一空白，我们提出名为SocialSense的新型框架，该框架利用大语言模型在现有社交网络之上诱导出以信念为中心的图结构，并借助基于图的传播机制捕捉社交动态。我们假设：通过连接具有相似信念但社交距离较远的用户，所诱导的图结构能使模型有效捕获响应模式。在零样本和全监督两种实验设置中，我们的方法均超越现有最优技术，验证了其在响应预测中的有效性。此外，分析表明该框架能有效处理未见用户和潜水用户场景，进一步凸显其鲁棒性与实际应用价值。