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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. 

The prevalence and spread of online misinformation during the 2020 US presidential election served to perpetuate a false belief in widespread election fraud. Though much research has focused on how social media platforms connected people to election-related rumors and conspiracy theories, less is known about the search engine pathways that linked users to news content with the potential to undermine trust in elections. In this paper, we present novel data related to the content of political headlines during the 2020 US election period. We scraped over 800,000 headlines from Google's search engine results pages (SERP) in response to 20 election-related keywords—10 general (e.g., "Ballots") and 10 conspiratorial (e.g., "Voter fraud")—when searched from 20 cities across 16 states. We present results from qualitative coding of 5,600 headlines focused on the prevalence of delegitimizing information. Our results reveal that videos (as compared to stories, search results, and advertisements) are the most problematic in terms of exposing users to delegitimizing headlines. We also illustrate how headline content varies when searching from a swing state, adopting a conspiratorial search keyword, or reading from media domains with higher political bias. We conclude with policy recommendations on data transparency that allow researchers to continue to monitor search engines during elections. 

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 removing false content and accounts that create or promote it. 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. By applying this model to a large corpus (10.5 million tweets) of misinformation events that occurred during the 2020 US election, we reveal that commonly proposed interventions are unlikely to be effective in isolation. However, our framework demonstrates that a combined approach can achieve a substantial reduction in the prevalence of misinformation. Our results highlight a practical path forward as misinformation online continues to threaten vaccination efforts, equity and democratic processes around the globe.

 

This paper introduces and presents a first analysis of a uniquely curated dataset of misinformation, disinformation, and rumors spreading on Twitter about the 2020 U.S. election. Previous research on misinformation—an umbrella term for false and misleading content—has largely focused either on broad categories, using a finite set of keywords to cover a complex topic, or on a few, focused case studies, with increased precision but limited scope. Our approach, by comparison, leverages real-time reports collected from September through November 2020 to develop a comprehensive dataset of tweets connected to 456 distinct misinformation stories from the 2020 U.S. election (our ElectionMisinfo2020 dataset), 307 of which sowed doubt in the legitimacy of the election. By relying on real-time incidents and streaming data, we generate a curated dataset that not only provides more granularity than a large collection based on a finite number of search terms, but also an improved opportunity for generalization compared to a small set of case studies. Though the emphasis is on misleading content, not all of the tweets linked to a misinformation story are false: some are questions, opinions, corrections, or factual content that nonetheless contributes to misperceptions. Along with a detailed description of the data, this paper provides an analysis of a critical subset of election-delegitimizing misinformation in terms of size, content, temporal diffusion, and partisanship. We label key ideological clusters of accounts within interaction networks, describe common misinformation narratives, and identify those accounts which repeatedly spread misinformation. We document the asymmetry of misinformation spread: accounts associated with support for President Biden shared stories in ElectionMisinfo2020 far less than accounts supporting his opponent. That asymmetry remained among the accounts who were repeatedly influential in the spread of misleading content that sowed doubt in the election: all but two of the top 100 ‘repeat spreader’ accounts were supporters of then-President Trump. These findings support the implementation and enforcement of ‘strike rules’ on social media platforms, directly addressing the outsized role of repeat spreaders.