The fragmentation of social media challenges how we might efficiently and effectively identify, understand, and counter harmful content. Prior work establishes frameworks for measuring problematic narratives, evaluating harms, and leveraging interdisciplinary theories and findings to design mitigating solutions. However, there is little understanding of these phenomena outside of mainstream platforms. This is particularly concerning given that alt-tech users have been observed to include insurrectionists, active shooters, and other extremists – often driven from mainstream platforms due to deplatforming and content moderation . Our work aims to characterize online narratives across alt-tech platforms. In particular, we highlight how rumoring and conspiracy theory narratives in the context of the 2022 U.S. elections impact social dynamics and inspire collective action. We gather a unique dataset of over 7,000 social media posts from Gab, Gettr, Parler, and Truth Social from which we derive prevalent narratives using natural language processing techniques. We then examine how the platform, affect, and engagement differ across context through the lens of narrative, social identity, and mobilization potential using mixed methods. Findings from our analyses show variation between how narratives support social identity conceptions of power and mobilization potential.

Public records laws are standard for state and federal governments across the U.S. Such laws rest on the core notion that transparency, enacted through access to governmental documentation, will ensure accountability and minimize governmental corruption. However, these laws can also be exploited to harass or burden government employees, including researchers at public universities. CSCW researchers have both an acute vulnerability to these requests, e.g., due to increasing politicization of topics core to our research agenda, and a unique opportunity to study challenges around public records requests as they intersect with the design and use of collaboration technologies. In this paper, we explore how a large number of public records requests (PRRs) affected the collaborative work of a large, multi-site academic research project. We find that — though participants believed PRRs were a valuable tool for government transparency — they added a complicated new dimension to distributed "work" which blends personal and professional dimensions. We explain how researchers interpreted these requests and adapted their communication techniques in response. We discuss how current technologies for communication and collaboration are unprepared to ensure personal privacy and security within adversarial research environments — and collaborative work environments more broadly. Finally, we highlight the misalignment between long-standing transparency laws and the current design of collaboration technologies and provide recommendations for updating these laws.

Recent work has demonstrated how content moderation practices on social media may unfairly affect marginalized individuals, for example by censoring women's bodies and misidentifying reclaimed terms as hate speech. This study documents and explores the direct experiences of marginalized creators who have been impacted by discriminatory content moderation on Instagram. Collaborating with our participants for over a year, we contribute five co-constructed narratives of discriminatory content moderation from advocates in trauma-informed care, LGBTQ+ sex education, anti-racism education, and beauty and body politics. In sharing these detailed personal accounts, not only do we shed light on their experiences with being blocked, banned, or deleted unfairly, but we delve deeper into the lasting impacts of these experiences to their livelihoods and mental health. Reflecting on their stories, we observe that content moderation on social media is deeply entangled with the situated experiences of offline discrimination. As such, we document how each participant experiences moderation through the lens of their often intersectional identities. Using participatory research methods, we collectively strategize ways to learn from these individual accounts and resist discriminatory content moderation, as well as imagine possibilities for repair and accountability.

This work explores how users navigate the opaque and ever-changing algorithmic processes that dictate visibility on Instagram through the lens of Attachment Theory. We conducted thematic analysis on 1,100 posts and comments on r/Instagram to understand how users engage in collective sensemaking with regards to Instagram’s algorithms, user-perceived punishments, and strategies to counteract algorithmic precarity. We found that the unpredictability in how Instagram rewards or punishes a user can lead to distress, hypervigilance, and a need to appease “the algorithm’’. We therefore frame these findings through Attachment Theory, drawing upon the metaphor of Instagram as an unreliable paternalistic figure that inconsistently rewards users [74]. User experiences are then contextualized through the lens of anxious, avoidant, disorganized, and secure attachment. We conclude by making suggestions for fostering secure attachment towards the Instagram algorithm, by suggesting potential strategies to help users successfully cope with uncertainty.

The 2020 United States (US) presidential election was - and has continued to be - the focus of pervasive and persistent mis- and disinformation spreading through our media ecosystems, including social media. This event has driven the collection and analysis of large, directed social network datasets, but such datasets can resist intuitive understanding. In such large datasets, the overwhelming number of nodes and edges present in typical representations create visual artifacts, such as densely overlapping edges and tightly-packed formations of low-degree nodes, which obscure many features of more practical interest. We apply a method, coengagement transformations, to convert such networks of social data into tractable images. Intuitively, this approach allows for parameterized network visualizations that make shared audiences of engaged viewers salient to viewers. Using the interpretative capabilities of this method, we perform an extensive case study of the 2020 United States presidential election on Twitter, contributing an empirical analysis of coengagement. By creating and contrasting different networks at different parameter sets, we define and characterize several structures in this discourse network, including bridging accounts, satellite audiences, and followback communities. We discuss the importance and implications of these empirical network features in this context. In addition, we release open-source code for creating coengagement networks from Twitter and other structured interaction data.

Prior work on transgender technology users in CSCW has primarily focused on how they interact with algorithms and communication technology, empirically identifying specific use cases and profiles, and speaking largely to the designers and developers of these platforms. This work has emphasized how trans people are excluded, harmed, and misrepresented in existing platforms, algorithms, and research methods. While these critiques are important, this paper explores what trans-inclusive quantitative methods could be by applying a participant- or user-driven approach. While the problem of trans-inclusive, -affirming, or -empowering research methods is not specifically a CSCW problem---as we directly confront by comparing and contrasting the perspectives of CSCW and conventional demography---we argue that a CSCW lens may be uniquely suited to addressing it. To this end, this paper makes several contributions: conceptually, we identify points of increasing convergence between conventional demographic research methods (and criticisms thereof) and CSCW, focusing on shared limitations surrounding how identity is handled in research; methodologically, we present a sketch of how these limitations might be addressed by using social network analysis to "triangulate" social identities while considering them relative and situated; empirically, we implement these methods in a case study of gender within the broader social context of Reddit and discuss the results.

The study of rumors has garnered wider attention as regulators and researchers turn towards problems of misinformation on social media. One goal has been to discover and implement mechanisms that promote healthy information ecosystems. Classically defined as regarding ambiguous situations, rumors pose the unique difficulty of intrinsic uncertainty around their veracity. Further complicating matters, rumors can serve the public when they do spread valuable true information. To address these challenges, we develop an approach that reifies "rumor proportions" as central to the theory of systems for managing rumors. We use this lens to advocate for systems that, rather than aiming to stifle rumors entirely or aiming to stop only false rumors, aim to prevent rumors from growing out of proportion relative to normative benchmark representations of intrinsic uncertainty.

Journalists are struggling to adapt to new conditions of news production and simultaneously encountering criticism for their role in spreading misinformation. Against the backdrop of this "crisis in journalism", this research seeks to understand how journalists are actually participating in the spread and correction of online rumors. We compare the engagement behaviors of journalists to non-journalists- and specifically other high visibility users-within five false rumors that spread on Twitter during three crisis events. Our findings show journalists engaging earlier than non-journalists in the spread and the correction of false rumors. However, compared to other users, journalists are (proportionally) more likely to deny false rumors. Journalists are also more likely to author original tweets and to be retweeted-underscoring their continued role in shaping the news. Interestingly, journalists scored high on "power user" measures, but were distinct from other power users in significant ways-e.g. by being more likely to deny rumors.

Widespread rumoring can hinder attempts to make sense of what is going on during disaster scenarios. Understanding how and why rumors spread in these contexts could assist in the design of systems that facilitate timely and accurate sensemaking. We address a basic question in this line: To what extent does rumor evolution occur (1) through reliance on a centralized information source, (2) in parallel information silos, or (3) through a web of complex informational interactions? We develop a conceptual model and associated analysis algorithms that allow us to distinguish between these possibilities. We analyze a case of rumoring on Twitter during the Boston Marathon Bombing. We find that rumor spreading was predominantly a parallel process in this case, which is consistent with a hypothesis that information silos may underlie the persistence of false rumors. Special attention towards detecting and resolving parallel information threads during collective sensemaking may hence be warranted.

This research examines Twitter discourse related to #BlackLivesMatter and police-related shooting events in 2016 through a mixed-method, interpretative approach. We construct a "shared audience graph", revealing structural and ideological disparities between two groups of participants (one on the political left, the other on the political right). We utilize an integrated networked gatekeeping and framing lens to examine how #BlackLivesMatter frames were produced--and how they were contested --by separate communities of supporters and critics. Among other empirical findings, this work demonstrates hashtags being used in diverse ways--e.g. to mark participation, assert individual identity, promote group identity, and support or challenge a frame. Considered from a networked gatekeeping perspective, we illustrate how hashtags can serve as channeling mechanisms, shaping trajectories of information flow. This analysis also reveals a right-leaning community of BlackLivesMatter critics to have a more well-defined group of crowdsourced elite who largely define their side's counter-frame.

Rumors are regular features of crisis events due to the extreme uncertainty and lack of information that often characterizes these settings. Despite recent research that explores rumoring during crisis events on social media platforms, limited work has focused explicitly on how individuals and groups express uncertainty. Here we develop and apply a flexible typology for types of expressed uncertainty. By applying our framework across six rumors from two crisis events we demonstrate the role of uncertainty in the collective sensemaking process that occurs during crisis events.

This paper examines how 'official' accounts participate in the propagation and correction of online rumors in the context of crisis events. Using an emerging method for interpretive analysis of 'big' social data, we investigate the spread of online rumors through digital traces-in this case, tweets. Our study suggests that official accounts can help to slow the spread of a rumor by posting a denial, and-supported by reflections from an organization that recently dealt with a rumor-crisis-offers best practices for organizations around social media strategies and protocols. Based on tweet data and connections to existing literature, we also demonstrate and discuss how mainstream media participate in rumoring, and note the role of a new breed of online media, 'breaking news' accounts. This analysis offers a complementary perspective to existing studies that use surveys and interviews to characterize the role official accounts play in online rumoring.

In this paper we highlight three distinct approaches to studying rumor dynamics-volume, exposure, and content production. Expanding upon prior work, which has focused on rumor volume, we argue that considering the size of the exposed population is a vital component of understanding rumoring. Additionally, by combining all three approaches we discover subtle features of rumoring behavior that would have been missed by applying each approach in isolation. Using a case study of rumoring on Twitter during a hostage crisis in Sydney, Australia, we apply a mixed-methods framework to explore rumoring and its consequences through these three lenses, focusing on the added dimension of exposure in particular. Our approach demonstrates the importance of considering both rumor content and the people engaging with rumor content to arrive at a more holistic understanding of communication dynamics. These results have implications for emergency responders and official use of social media during crisis management.

In addition to posting news and status updates, many Twitter users post questions that seek various types of subjective and objective information. These questions are often labeled with 'Q&A' hashtags, such as #lazyweb or #twoogle. We surveyed Twitter users and found they employ these Q&A hashtags both as a topical signifier (this tweet needs an answer!) and to reach out to those beyond their immediate followers (a community of helpful tweeters who monitor the hashtag). However, our log analysis of thousands of hashtagged Q&A exchanges reveals that nearly all replies to hashtagged questions come from a user's immediate follower network, contradicting users' beliefs that they are tapping into a larger community by tagging their question tweets. This finding has implications for designing next-generation social search systems that reach and engage a wide audience of answerers.