Introduction
“Your beliefs become your thoughts. Your thoughts become your words. Your words become your actions." — Mahatma Gandhi

Automatic response forecasting (Figure 1 ) on receivers for news media is a burgeoning field of research that enables numerous influential applications, such as offering content producers a way to efficiently estimate the potential impact of their messages (aiding the prevention of unexpected negative outcomes) and supporting human writers in attaining their communication goals Sun et al. ( 2023 ) for risk management. This direction is especially important nowadays as the proliferation of AI-generated misinformation, propaganda, and hate speech are becoming increasingly elusive to detection Hsu and Thompson ( 2023 ); Owen and Zahn ( 2023 ) . In this context, accurately forecasting the responses from different audiences or communities to news media messages becomes critical.

One of the primary challenges in personalized response forecasting lies in developing effective user representations. A crucial aspect to consider when representing a user is the integration of social dynamics (e.g., social interactions around a user) as well as their individual beliefs and interests. This becomes particularly relevant for users who lack explicit profiles or historical activities (commonly referred to as lurkers). Previous efforts, however, have yet to explore the types of structural information that are helpful and how to best utilize such information Lin and Chen ( 2008 ); Giachanou et al. ( 2018 ); Yang et al. ( 2019 ); Wu et al. ( 2021 ) .

During our preliminary analysis, we observed that users who share similar beliefs, specifically social values, are often situated in distant communities within the explicit social network. To provide further context, our findings reveal that a significant portion (over 44.6%) of users in the network data we collected for our experiment share beliefs with other users who are at least two hops away in the network. This emphasizes the importance of considering the connections between users with similar beliefs, even if they are not directly linked in the social network. Furthermore, previous research has indicated that user history plays a significant role in the model’s performance. However, it is often directly utilized without processing in existing approaches, leading to the introduction of noise in the modeling process.

Motivated by these findings, we introduce SocialSense (where Sense refers to the understanding and perception of social dynamics and behaviors within the online realm), a novel framework for modeling user beliefs and the social dynamics surrounding users in a social network. In this work, we conduct experiments using the SocialSense framework in the context of response forecasting. Our approach aims to capture the pattern of how “similar neighbors respond to similar news similarly”. To harness the potential of network features, we curated a new user-user graph comprising 18k users from Twitter (the data will be anonymized when released), augmenting the original dataset Sun et al. ( 2023 ) . The SocialSense framework consists of three key stages: (1) inducing latent user personas using the Large Language Model (e.g., ChatGPT Liu et al. ( 2023 ) ), (2) building a belief-centered network on top of the existing social network, and (3) propagating information across multiple levels.

We demonstrate the effectiveness of our method through experiments on the dataset from Sun et al. ( 2023 ) . Our results show that our framework outperforms existing baselines consistently across metrics in both zero-shot and fully-supervised settings. We further conduct a detailed analysis to address research questions concerning the model’s generalizability to unseen users and its predictive capabilities for lurkers. Our findings reveal two additional key insights: (1) the model performs exceptionally well in scenarios involving lurkers, outperforming the baseline by over 10% accuracy score in sentiment polarity forecasting, and, (2) compared to baseline approaches, the model exhibits consistently better generalization capabilities when applied to unseen users. Additionally, our analysis underscores the significance of various components within the belief-augmented social network, revealing that both the belief-centered graph and the user-news interaction network play vital roles in determining the network’s overall performance.