Misinformation and disinformation about elections remain pressing concerns for researchers, policymakers, and the public. Critics, however, argue that fears surrounding these issues are exaggerated due to a lack of evidence of impact. This debate highlights the challenges inherent in assessing the impacts of misinformation, as the drivers of false and misleading content often exist in the context of a specific claim. To address this issue, we examined false and misleading information surrounding the 2020 and 2022 U.S. national elections, focusing on the contextual features of online conversations that fueled various rumors. We developed two qualitative codebooks, creating the second after realizing that the first, which labeled individual tweets, failed to capture broader rumoring dynamics. By integrating multi-layered qualitative coding with thematic analysis and quantitative visualizations, we show how influencers, political elites, and audiences collaboratively told deep stories from 2020 through 2022. As these stories were told, audiences interpreted events in 2022 through the lens of the 2020 story, guided by influencers' cues, leading to an evolution in storytelling style between the two election cycles. This ongoing performance was tailored to align with the incentive structures, affordances, and attention economy of social media. We combine deep stories with theories of collective sensemaking and rumoring, creating a framework to better assess the contextual features surrounding false and misleading information.

Pervasive falsehoods that erode trust in election processes are of increasing concern to democracies around the world. Misleading claims like these are often understood as simply ''getting the facts wrong''. Using a grounded, interpretative, mixed-method approach to study Twitter activity during the 2022 U.S. Midterm Election in Arizona, our work paints a more nuanced picture. We adapt Klein's data-frame theory of collective sensemaking to online rumors, demonstrating how misleading claims about election administration take shape online through interactions between (often factual) evidence and frames. We introduce a methodological approach for analyzing rumors through this evidence-frame lens and provide insights into the dynamics of online rumoring around claims of ''rigged elections''. Our work highlights how rumors are as much about political framing as they are about faulty facts, and locates the crux of the problem of misinformation in the interactions with and between evidence and distorted political frames.

Individuals seeking out information about human-trafficking and anti-trafficking efforts are increasingly turning to social media as an informational source. However, a lack of traditional informational gatekeeping online has allowed for the rapid proliferation of misinformation via social media. This has been clearly evidenced within the realm of human trafficking by the spread of conspiracy theories instigated by the QAnon-led campaign #SaveTheChildren. Through in-depth interviews with members of the public and professionals involved in anti-trafficking activism we explore how individuals find trustworthy information about human trafficking in light of the public spread of misinformation. Our findings highlight the centrality of distrust as a driving force behind information-seeking on social media. Further, we highlight the tensions that arise from using social media as a primary resource within anti-trafficking education and the limitations of interventions to slow the spread of trafficking-related misinformation. This work provides contextual knowledge for researchers looking to better understand the real-world impacts of misinformation and looking to design better interventions into digital information disorder.

Claims of election fraud throughout the 2020 U.S. Presidential Election and during the lead up to the January 6, 2021 insurrection attempt have drawn attention to the urgent need to better understand how people interpret and act on disinformation. In this work, we present three primary contributions: (1) a framework for understanding the interaction between participatory disinformation and informal and tactical mobilization; (2) three case studies from the 2020 U.S. election analyzed using detailed temporal, content, and thematic analysis; and (3) a qualitative coding scheme for understanding how digital disinformation functions to mobilize online audiences. We combine resource mobilization theory with previous work examining participatory disinformation campaigns and "deep stories" to show how false or misleading information functioned to mobilize online audiences before, during, and after election day. Our analysis highlights how users on Twitter collaboratively construct and amplify alleged evidence of fraud that is used to facilitate action, both online and off. We find that mobilization is dependent on the selective amplification of false or misleading tweets by influencers, the framing around those claims, as well as the perceived credibility of their source. These processes are a self-reinforcing cycle where audiences collaborate in the construction of a misleading version of reality, which in turn leads to offline actions that are used to further reinforce a manufactured reality. Through this work, we hope to better inform future interventions.