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Understanding the spread of online rumors is a pressing societal challenge and an active area of research across domains. In the context of the 2022 U.S. midterm elections, one influential social media platform for sharing information -- including rumors that may be false, misleading, or unsubstantiated -- was Twitter (now renamed X). To increase understanding of the dynamics of online rumors about elections, we present and analyze a dataset of 1.81 million Twitter posts corresponding to 135 distinct rumors which spread online during the midterm election season (September 5 to December 1, 2022). We describe how this data was collected, compiled, and supplemented, and provide a series of exploratory analyses along with comparisons to a previously-published dataset on 2020 election rumors. We also conduct a mixed-methods analysis of three distinct rumors about the election in Arizona, a particularly prominent focus of 2022 election rumoring. Finally, we provide a set of potential future directions for how this dataset could be used to facilitate future research into online rumors, misinformation, and disinformation.

When designing multi-stakeholder privacy systems, it is important to consider how different groups of social media users have different goals and requirements for privacy. Additionally, we must acknowledge that it is important to keep in mind that even a single creator's needs can change as their online visibility and presence shifts, and that robust multi-stakeholder privacy systems should account for these shifts. Using the framework of contextual integrity, we explain a theoretical basis for how to evaluate the potential changing privacy needs of users as their profiles undergo a sudden rise in online attention, and ongoing projects to understand these potential shifts in perspectives., Comment: 4 pages, presented at the "Multi-Stakeholder Privacy and Safety on Content Creation Platforms" workshop at the ACM DIS 2023 conference

Traditional research ethics has mainly and rightly been focused on making sure that participants are treated safely, justly, and ethically, to avoid the violation of their rights or putting participants in harm's way. Information integrity research within CSCW has also correspondingly mainly focused on these issues, and the focus of internet research ethics has primarily focused on increasing protections of participant data. However, as branches of internet research focus on more fraught contexts such as information integrity and problematic information, more explicit consideration of other ethical frames and subjects is warranted. In this workshop paper, we argue that researcher protections should be more explicitly considered and acknowledged in these studies, and should be considered alongside more standard ethical considerations for participants and for broader society., Comment: 5 pages, presented at the pluralism@CSCW workshop at ACM CSCW 2023

What types of governance arrangements makes some self-governed online groups more vulnerable to disinformation campaigns? To answer this question, we present a qualitative comparative analysis of the Croatian and Serbian Wikipedia editions. 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 Wikipedia is roughly similar in size and age, shares many linguistic and cultural features, and faced similar threats, it seems to have largely avoided this fate. Based on a grounded theory analysis of interviews with members of both 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., Comment: 26 pages, 2 figures. Accepted for publication in Proceedings of the ACM on Human-Computer Interaction (CSCW 2024)

The 2020 United States 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., Comment: Accepted for publication at ICWSM '23

In the 21st Century information environment, adversarial actors use disinformation to manipulate public opinion. The distribution of false, misleading, or inaccurate information with the intent to deceive is an existential threat to the United States--distortion of information erodes trust in the socio-political institutions that are the fundamental fabric of democracy: legitimate news sources, scientists, experts, and even fellow citizens. As a result, it becomes difficult for society to come together within a shared reality; the common ground needed to function effectively as an economy and a nation. Computing and communication technologies have facilitated the exchange of information at unprecedented speeds and scales. This has had countless benefits to society and the economy, but it has also played a fundamental role in the rising volume, variety, and velocity of disinformation. Technological advances have created new opportunities for manipulation, influence, and deceit. They have effectively lowered the barriers to reaching large audiences, diminishing the role of traditional mass media along with the editorial oversight they provided. The digitization of information exchange, however, also makes the practices of disinformation detectable, the networks of influence discernable, and suspicious content characterizable. New tools and approaches must be developed to leverage these affordances to understand and address this growing challenge., Comment: A Computing Community Consortium (CCC) white paper, 5 pages

The aim of this paper is to demonstrate that the current understanding of crowdsourcing may not be broad enough to capture the diversity of crowd work during disasters, or specific enough to highlight the unique dynamics of information organizing by the crowd in that context. In making this argument, this paper first unpacks the crowdsourcing term, examining its roots in open source development and outsourcing business models, and tying it to related concepts of human computation and collective intelligence. The paper then attempts to characterize several examples of crowd work during disasters using current definitions of crowdsourcing and existing models for human computation and collective intelligence, exposing a need for future research towards a framework for understanding crowd work., Comment: Presented at Collective Intelligence conference, 2012 (arXiv:1204.2991)