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Supplemental material, sj-docx-1-sms-10.1177_20563051231177943 for Influence and Improvisation: Participatory Disinformation during the 2020 US Election by Kate Starbird, Renée DiResta and Matt DeButts in Social Media + Society

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

Misinformation online poses a range of threats, from subverting democratic processes to undermining public health measures. Proposed solutions range from encouraging more selective sharing by individuals, to platform removal of false content and accounts that create or promote it. How, whether, and which strategies to implement depends on their relative and combined ability to reduce viral misinformation spread at plausible levels of enforcement. Here we provide a framework to evaluate interventions aimed at reducing viral misinformation online both in isolation and when used in combination. We begin by deriving a generative model of viral misinformation spread, inspired by research on infectious disease. Applying this model to a large corpus of misinformation events that occurred during the 2020 US election, we reveal that commonly proposed interventions--including removal of content, virality circuit breakers, nudges, and banning---are unlikely to be effective in isolation without extreme censorship or major breakthroughs in how to control misinformation. However, our framework demonstrates that a combined approach can achieve a substantial ~50% reduction in the spread of misinformation. Our results challenge claims that combating misinformation will require new ideas or high costs to user expression. Instead, we highlight a practical path forward as misinformation online continues to threaten vaccination efforts, equity, and democratic processes around the globe.

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)

In this paper, we propose a low-tech solution for use by microbloggers that could enhance their ability to rapidly produce parsable, crisis-relevant information in mass emergencies. We build upon existing research on the use of social media during mass emergencies and disasters. Our proposed intervention aims to leverage the affordances of mobile microblogging and the drive to support citizen reporting within current behavioral Twitter-based microblogging practice. We introduce a prescriptive, tweet-based syntax that could increase the utility of information generated during emergencies by gently reshaping current behavioral practice. This offering is grounded in an understanding of current trends in norm evolution of Twitter use, an evolution that has progressed quickly but appears to be stabilizing around specific textual conventions.