In the era of digital communication, misinformation on social media threatens the foundational trust in these platforms. While myriad measures have been implemented to counteract misinformation, the complex relationship between these interventions and the multifaceted dynamics of trust and distrust on social media remains underexplored. To bridge this gap, we surveyed 1,769 participants in the U.S. to gauge their trust and distrust in social media and examine their experiences with anti-misinformation features. Our research demonstrates how trust and distrust in social media are not simply two ends of a spectrum; but can also co-exist, enriching the theoretical understanding of these constructs. Furthermore, participants exhibited varying patterns of trust and distrust across demographic characteristics and platforms. Our results also show that current misinformation interventions helped heighten awareness of misinformation and bolstered trust in social media, but did not alleviate underlying distrust. We discuss theoretical and practical implications for future research.

With the advancement of Large Language Models (LLMs), they are increasingly being used as a backend for interactive virtual agents and assistants. Therefore, a critical social skill for these agents is Theory of Mind (ToM): the ability to model and reason about other agents. Research has investigated ToM in LLMs using standard, modified, and extended versions of false-belief tasks. These tests include explicit prompts asking LLMs to answer questions about other agents. However, in real situations, people have to use ToM unprompted to navigate social life. Additionally, oftentimes, people have to rely on nonverbal cues such as facial expressions. This work seeks to address this gap by studying implicit ToM in LLMs in a negotiation task. In negotiation, agents have to implicitly reason about other agents to reach an agreed-upon best possible deal. We conducted the negotiation experiment by prompting different LLMs to roleplay as characters and pitting them against rule-based agents that may respond with different facial expressions. We measure and compare the outcomes of the negotiation across models. Our results show that strong LLMs like GPT-4 turbo and Claude 3 Opus can perform decently and adjust their offers based on access to facial expression information, but weaker models are far behind. Our work contributes to our understanding of LLMs’ capabilities and limitations for serving as intelligent and interactive agents.

Do people trust social media? If so, why, in what contexts, and how does that trust impact their lives? Researchers, companies, and journalists alike have increasingly investigated these questions, which are fundamental to understanding social media interactions and their implications for society. However, trust in social media is a complex concept, and there is conflicting evidence about the antecedents and implications of trusting social media content, users, and platforms. More problematic is that we lack basic agreement as to what trust means in the context of social media. Addressing these challenges, we conducted a systematic review to identify themes and challenges in this field. Through our analysis of 70 papers, we contribute a synthesis of how trust in social media is defined, conceptualized, and measured, a summary of trust antecedents in social media, an understanding of how trust in social media impacts behaviors and attitudes, and directions for future work.

Abstract We stumble upon new and repeating information daily. As information comes from many sources, social media continues to play a predominant role in disseminating information, ultimately impacting individuals’ perceptions and behaviors. A prime example of this impact was observed during the COVID-19 pandemic, in which social media use was influencing willingness to receive the COVID-19 vaccine. While studies on this relationship between social media use and vaccination intent have been widely investigated, less is known about the mechanisms that link these two variables, specifically the types of information seen on social media platforms and the effects of these different types of information. In this exploratory study, we demonstrate the mediator role of information exposure (to include both types of information and frequency) between social media use and vaccination intent. Our results show that different types of information mediate this relationship differently and demonstrate how these relationships were further moderated by the income level of the participant. We conclude with the implications of these findings and how our findings can inform the direction of future research within the field of human–computer interaction.

People often encounter highly stressful, emotion-evoking situations. Modeling and predicting people's behavior in such situations, how they cope, is a critical research topic. To that end, we propose a computational model of coping that casts Lazarus's theory of coping into a Partially Observable Markov Decision Process (POMDP) framework. This includes an appraisal process that models the factors leading to stress by assessing a person's relation to the environment and a coping process that models how people seek to reduce stress by directly altering the environment or changing one's beliefs and goals. We evaluated the model's assumptions in the context of a high-stress situation, hurricanes. We collected questionnaire data from major U.S. hurricanes in 2018 to evaluate the model's features for appraisal calculation. We also conducted a series of controlled experiments simulating a hurricane experience to investigate how people change their beliefs and goals to cope with the situation. The results support the model's assumptions showing that the proposed features are significantly associated with the evacuation decisions and people change their beliefs and goals to cope with the situation.

People experience many stressful, emotion-evoking situations in everyday life. How they cope with these situations is crucial to their well-being. Research shows that people may change their beliefs to perceive the situations in a better, less-stressful light. Therefore, understanding how people change their beliefs to cope with stress and emotion is an important research question. Toward that end, we model coping, based on Lazarus's appraisal theory of emotion, as a two-step decision problem. However, people do not hallucinate arbitrary alternative realities in order to reduce stress. So a central challenge here is to model the constraints on belief change. We specifically stipulate two key factors influencing the degree of belief change: the utility of holding the alternative belief and constraints on changing a belief based on its underlying uncertainty. To investigate these factors and the model's assumptions, we applied the model to a simple hurricane situation and conducted an experiment based on this situation, where participants observed hurricane information and reported their beliefs about it. We found that when the hurricane worsens, those who stayed believe the hurricane to be less severe than the most likely outcome from the information and those who evacuated. The results also show that the uncertainty of information and the utility of the beliefs about that information is related and together determine how emotion-focused coping alters the person's beliefs. Overall, the results support the assumptions and predictions of the model. These findings illustrate the relevance of applying a decision-making model analysis to coping.

During crises like COVID-19, individuals are inundated with conflicting and time-sensitive information that drives a need for rapid assessment of the trustworthiness and reliability of information sources and platforms. This parallels evolutions in information infrastructures, ranging from social media to government data platforms. Distinct from current literature, which presumes a static relationship between the presence or absence of trust and people’s behaviors, our mixed-methods research focuses on situated trust, or trust that is shaped by people’s information-seeking and assessment practices through emerging information platforms (e.g., social media, crowdsourced systems, COVID data platforms). Our findings characterize the shifts in trustee (what/who people trust) from information on social media to the social media platform(s), how distrust manifests skepticism in issues of data discrepancy, the insufficient presentation of uncertainty, and how this trust and distrust shift over time. We highlight the deep challenges in existing information infrastructures that influence trust and distrust formation.