Search for: All records

What types of governance arrangements make some self-governed online groups more vulnerable to disinformation campaigns? We present a qualitative comparative analysis of the Croatian and Serbian Wikipedia editions to answer this question. We do so because between at least 2011 and 2020, the Croatian language version of Wikipedia was taken over by a small group of administrators who introduced far-right bias and outright disinformation. Dissenting editorial voices were reverted, banned, and blocked. Although Serbian, Bosnian, and Serbo-Croatian Wikipedias share many linguistic and cultural features, and faced similar threats, they seem to have largely avoided this fate. Based on a grounded theory analysis of interviews with members of these communities and others in cross-functional platform-level roles, we propose that the convergence of three features---high perceived value as a target, limited early bureaucratic openness, and a preference for personalistic, informal forms of organization over formal ones---produced a window of opportunity for governance capture on Croatian Wikipedia. Our findings illustrate that online community governing infrastructures can play a crucial role in systematic disinformation campaigns and other influence operations. 

Social media platform affordances allow users to interact with content and with each other in diverse ways. For example, on Twitter,¹users can like, reply, retweet, or quote another tweet. Though it’s clear that these different features allow various types of interactions, open questions remain about how these different affordances shape the conversations. We examine how two similar, but distinct conversational features on Twitter — specifically reply vs. quote — are used differently. Focusing on the polarized discourse around Robert Mueller’s congressional testimony in July 2019, we look at how these features are employed in conversations between politically aligned and opposed accounts. We use a mixed methods approach, employing grounded qualitative analysis to identify the different conversational and framing strategies salient in that discourse and then quantitatively analyzing how those techniques differed across the different features and political alignments. Our research (1) demonstrates that the quote feature is more often used to broadcast and reply is more often used to reframe the conversation; (2) identifies the different framing strategies that emerge through the use of these features when engaging with politically aligned vs. opposed accounts; (3) discusses how reply and quote features may be re-designed to reduce the adversarial tone of polarized conversations on Twitter-like platforms. 

The COVID-19 pandemic provides a unique opportunity to study science communication and, in particular, the transmission of consensus. In this study, we show how “science communicators,” writ large to include both mainstream science journalists and practiced conspiracy theorists, transform scientific evidence into two dueling consensuses using the effectiveness of masks as a case study. We do this by compiling one of the largest, hand-coded citation datasets of cross-medium science communication, derived from 5 million Twitter posts of people discussing masks. We find that science communicators selectively uplift certain published works while denigrating others to create bodies of evidence that support and oppose masks, respectively. Anti-mask communicators in particular often use selective and deceptive quotation of scientific work and criticize opposing science more than pro-mask communicators. Our findings have implications for scientists, science communicators, and scientific publishers, whose systems of sharing (and correcting) knowledge are highly vulnerable to what we term adversarial science communication. 

As a research tradition, participatory design (PD) tends to focus on power dynamics where researchers hold greater power than participants. This paper uses design fiction to consider what this tendency overlooks by examining settings where participants may exist in multiple power relationships simultaneously implicated by the research, specifically focusing on the contexts of misinformation, disinformation, and online hate (M/D/OH). Drawing from existing literature in M/D/OH, we present a series of imaginary method abstracts that prompt questions for researchers to reflect on as they adapt PD techniques for new, different contexts. We highlight three value tensions—authenticity, reciprocity, and impact—integral to sustaining a concern for responsibility in PD scholarship. We end with reflections and potential considerations for responsibly applying PD and design fiction methods in M/D/OH settings. 

Decades-old research about how and why people share rumors is even more relevant in a world with social media. 

In this article, we introduce the concept of a spotlight social media post —a post that receives an unexpected burst of attention—and explore how such posts reveal salient aspects of online collective sensemaking and attention dynamics during a crisis event. Specifically, we examine the online conversation surrounding a false missile alert in Hawaii in January 2018. Through a mixed-methods analysis and visualizations, our research uncovers mechanisms that lead to rapid attention gains, such as spotlighting —when a user with existing influence confers attention by sharing others’ content with their audience. We highlight how spotlight social media posts (specifically spotlight tweets ) are distinct from other heavily shared content and that they offer insight into previously overlooked patterns in information exchange. We additionally reveal that attention dynamics may alter the social position of spotlight post authors immediately afterward (and possibly in the long term). We argue that spotlight social media posts offer a productive window for understanding online collective sensemaking, and we discuss how this can inform social media platform design and serve as a basis of future research. 

The 2020 US election was accompanied by an effort to spread a false meta-narrative of widespread voter fraud. This meta-narrative took hold among a substantial portion of the US population, undermining trust in election procedures and results, and eventually motivating the events of 6 January 2021. We examine this effort as a domestic and participatory disinformation campaign in which a variety of influencers—including hyperpartisan media and political operatives—worked alongside ordinary people to produce and amplify misleading claims, often unwittingly. To better understand the nature of participatory disinformation, we examine three cases of misleading claims of voter fraud, applying an interpretive, mixed method approach to the analysis of social media data. Contrary to a prevailing view of such campaigns as coordinated and/or elite-driven efforts, this work reveals a more hybrid form, demonstrating both top-down and bottom-up dynamics that are more akin to cultivation and improvisation. 

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. 

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. 

Well-intentioned users sometimes enable the spread of misinformation due to limited context about where the information originated and/or why it is spreading. Building upon recommendations based on prior research about tackling misinformation, we explore the potential to support media literacy through platform design. We develop and design an intervention consisting of a tweet trajectory-to illustrate how information reached a user-and contextual cues-to make credibility judgments about accounts that amplify, manufacture, produce, or situate in the vicinity of problematic content (AMPS). Using a research through design approach, we demonstrate how the proposed intervention can help discern credible actors, challenge blind faith amongst online friends, evaluate the cost of associating with online actors, and expose hidden agendas. Such facilitation of credibility assessment can encourage more responsible sharing of content. Through our findings, we argue for using trajectory-based designs to support informed information sharing, advocate for feature updates that nudge users with reflective cues, and promote platform-driven media literacy.