This study addresses two currently open questions about how behaviors of online community members influence others’ responses to misinformation. First, in contrast to prior work, it directly measures norm perception to address whether (1) norm perception actually acts as a mediator, (2) others’ behaviors directly influence individuals’ responses to misinformation, (3) both direct and mediated effects occur. Second, it investigates norm perceptions about a behavior that is not readily observable in online communities, but is prone to misinformation, specifically, vaccination. To do so, it experimentally manipulates the prevalence of communicating about vaccination (an unobservable behavior) within an online community. The results demonstrate no evidence of a direct effect—the causal relationship between prevalence of communicating a behavior and intentions to respond to misinformation only occurs via norm perception as a mediator. The paper highlights implications of these findings for designing community-centered interventions to influence perceived norms, thereby mitigating misinformation spread and impacts.

Social norms play a significant role in how conspiratorial content and related misinformation impact online communities. However, less is understood about the mechanisms by which particular aspects of a community may drive perceptions of social norms in the community. Using anti-vaccine conspiracies as a testbed, this paper experimentally examines three such features and their relationships : prevalence of conspiratorial content, community response, and explicit community rules. Results show that prevalence of content has a significant effect on norm perceptions, while the results did not support the effects of explicit rule on norm perceptions. However, these effects can be mitigated by the way a community responds to such content. Furthermore, perceived norms also influence other expectations about the community, from escalated behaviors to belief in other conspiracy theories. The paper concludes by highlighting the implications of these findings for online platform design, for community governance, and for future research about the relationships among conspiratorial content and norm perceptions.

Prior work has identified a variety of factors that drive the way people identify and respond to misinformation. Such factors include confirmation bias, perceived credibility of the information source, individual media literacy, social norms, and others. This paper reviews the interventions designed to address misinformation and examines how various underlying mechanisms of response to misinformation are operationalized and implemented in the reviewed interventions. Key findings show that most prior work to address misinformation heavily focuses on individual pieces of misinformation and the actions individuals take in response to those individual pieces. These individualistic approaches, we argue, overlook the other drivers of responses to misinformation, such as individuals' prior beliefs and the social contexts in which misinformation is encountered. Additionally, the analysis shows that an individualistic focus on misinformation draws attention away from the systemic nature and consequences of misinformation. This paper argues that to overcome the limitation of individualistic approaches to addressing misinformation, future interventions need to expand their scope beyond individualistic approaches. As one way to do so, it discusses leveraging the impacts of community factors that impact the spread and impacts of misinformation. The paper concludes by using social norms as an example to illustrate how a focus on community factors might work in practice.

As AI-mediated communication (AI-MC) becomes more prevalent in everyday interactions, it becomes increasingly important to develop a rigorous understanding of its effects on interpersonal relationships and on society at large. Controlled experimental studies offer a key means of developing such an understanding, but various complexities make it difficult for experimental AI-MC research to simultaneously achieve the criteria of experimental realism, experimental control, and scalability. After outlining these methodological challenges, this paper offers the concept of methodological middle spaces as a means to address these challenges. This concept suggests that the key to simultaneously achieving all three of these criteria is to abandon the perfect attainment of any single criterion. This concept's utility is demonstrated via its use to guide the design of a platform for conducting text-based AI-MC experiments. Through a series of three example studies, the paper illustrates how the concept of methodological middle spaces can inform the design of specific experimental methods. Doing so enabled these studies to examine research questions that would have been either difficult or impossible to investigate using existing approaches. The paper concludes by describing how future research could similarly apply the concept of methodological middle spaces to expand methodological possibilities for AI-MC research in ways that enable contributions not currently possible.