Matthew R. DeVerna

Research

Peer-reviewed publications

1. A multi-platform collection of social media posts about the 2022 U.S. midterm elections

   Abstract PDF

   Rachith Aiyappa^†, Matthew R. DeVerna^†, Manita Pote^†, Bao Tran Truong^†, Wanying Zhao^†, David Axelrod, Aria Pessianzadeh, Zoher Kachwala, Munjung Kim, Ozgur Can Seckin, Minsuk Kim, Sunny Gandhi, Amrutha Manikonda, Francesco Pierri, Filippo Menczer, Kai-Cheng Yang

   Proceedings of the International AAAI Conference on Web and Social Media. 2023.

   A multi-platform collection of social media posts about the 2022 U.S. midterm elections

   Social media are utilized by millions of citizens to discuss important political issues. Politicians use these platforms to connect with the public and broadcast policy positions. Therefore, data from social media has enabled many studies of political discussion. While most analyses are limited to data from individual platforms, people are embedded in a larger information ecosystem spanning multiple social networks. Here we describe and provide access to the Indiana University 2022 U.S. Midterms Multi-Platform Social Media Dataset (MEIU22), a collection of social media posts from Twitter, Facebook, Instagram, Reddit, and 4chan. MEIU22 links to posts about the midterm elections based on a comprehensive list of keywords and tracks the social media accounts of 1,210 candidates from October 1 to December 25, 2022. We also publish the source code of our pipeline to enable similar multi-platform research projects.

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2. One year of COVID-19 vaccine misinformation on Twitter

   Abstract PDF

   Francesco Pierri, Matthew R. DeVerna, Kai-Cheng Yang, David Axelrod, John Bryden, and Filippo Menczer

   Journal of Medical Internet Research. 2023.

   One year of COVID-19 vaccine misinformation on Twitter

   Background
   Vaccinations play a critical role in mitigating the impact of COVID-19 and other diseases. Past research links misinformation, including that which spreads on social media, to increased hesitancy and lower vaccination rates. Gaps remain in our knowledge on the main drivers of vaccine misinformation on social media and effective ways to intervene.
   Objective
   This study explores COVID-19 vaccine misinformation circulating on Twitter during 2021, when vaccines were being released to the public in an effort to mitigate the global pandemic. Our work studies the prevalence of information originating from low-credibility news websites and YouTube videos, and identifies the main spreaders of vaccine misinformation.
   Methods
   We collected almost 300M English-language tweets related to COVID-19 vaccines using a list of over 80 relevant keywords over a period of 12 months. We then extracted and labeled news articles at the source level, based on third-party lists of low-credibility and mainstream news sources, and measured the prevalence of different kinds of information. We also considered suspicious YouTube videos shared on Twitter. To identify spreaders of vaccine misinformation, we focused on verified Twitter accounts and employed a bot detection algorithm to identify accounts that are likely automated.
   Results
   Our findings show a low prevalence of low-credibility information compared to mainstream news. However, most popular low-credibility sources had reshare volumes comparable to many mainstream sources, and larger volumes than authoritative sources such as the U.S. Centers for Disease Control and Prevention and the World Health Organization. Throughout the year, we observed an increasing trend in the prevalence of low-credibility news relative to mainstream news about vaccines. We also observed a considerable amount of suspicious YouTube videos shared on Twitter. We found that tweets by a small group of about 800 “superspreaders” verified by Twitter accounted for approximately 35% of all reshares of misinformation on the average day, with the top superspreader (@RobertKennedyJr) being responsible for over 13% of retweets. We also found that low-credibility news and suspicious YouTube videos were more likely to be shared by automated accounts.

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3. Rumors in Retweet: Ideological Asymmetry in the Failure to Correct Misinformation

   Abstract PDF

   Matthew R. DeVerna, Andy Guess, Adam Berinsky, Joshua Tucker, and John Jost

   Personality and Social Psychology Bulletin. 2022.

   Rumors in Retweet: Ideological Asymmetry in the Failure to Correct Misinformation

   We used supervised machine-learning techniques to examine ideological asymmetries in online rumor transmission. Although liberals were more likely than conservatives to communicate in general about the 2013 Boston Marathon bombings (Study 1, N = 26,422) and 2020 death of the sex trafficker Jeffrey Epstein (Study 2, N = 141,670), conservatives were more likely to share rumors. Rumor-spreading decreased among liberals following official correction, but it increased among conservatives. Marathon rumors were spread twice as often by conservatives pre-correction, and nearly 10 times more often post-correction. Epstein rumors were spread twice as often by conservatives pre-correction, and nearly, eight times more often post-correction. With respect to ideologically congenial rumors, conservatives circulated the rumor that the Clinton family was involved in Epstein’s death 18.6 times more often than liberals circulated the rumor that the Trump family was involved. More than 96% of all fake news domains were shared by conservative Twitter users.

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4. Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal

   Abstract PDF

   Francesco Pierri, Brea L. Perry, Matthew R. DeVerna, Kai-Cheng Yang, Alessandro Flammini, Filippo Menczer, and John Bryden

   Scientific Reports. 2022.
   Listed in Scientific Reports Top 100 most downloaded articles of 2022
   Cited in the 2023 Economic Report of the President

   Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal

   Widespread uptake of vaccines is necessary to achieve herd immunity. However, uptake rates have varied across U.S. states during the first six months of the COVID-19 vaccination program. Misbeliefs may play an important role in vaccine hesitancy, and there is a need to understand relationships between misinformation, beliefs, behaviors, and health outcomes. Here we investigate the extent to which COVID-19 vaccination rates and vaccine hesitancy are associated with levels of online misinformation about vaccines. We also look for evidence of directionality from online misinformation to vaccine hesitancy. We find a negative relationship between misinformation and vaccination uptake rates. Online misinformation is also correlated with vaccine hesitancy rates taken from survey data. Associations between vaccine outcomes and misinformation remain significant when accounting for political as well as demographic and socioeconomic factors. While vaccine hesitancy is strongly associated with Republican vote share, we observe that the effect of online misinformation on hesitancy is strongest across Democratic rather than Republican counties. Granger causality analysis shows evidence for a directional relationship from online misinformation to vaccine hesitancy. Our results support a need for interventions that address misbeliefs, allowing individuals to make better-informed health decisions.

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5. CoVaxxy: A Collection of English-Language Twitter Posts About COVID-19 Vaccines

   Abstract PDF

   Matthew R. DeVerna, Francesco Pierri, Bao Tran Truong, John Bollenbacher, David Axelrod, Niklas Loynes, Christopher Torres-Lugo, Kai-Cheng Yang, Filippo Menczer, and John Bryden

   Proceedings of the International AAAI Conference on Web and Social Media. 2021.

   CoVaxxy: A Collection of English-Language Twitter Posts About COVID-19 Vaccines

   With a substantial proportion of the population currently hesitant to take the COVID-19 vaccine, it is important that people have access to accurate information. However, there is a large amount of low-credibility information about vaccines spreading on social media. In this paper, we present the CoVaxxy dataset, a growing collection of English-language Twitter posts about COVID-19 vaccines. Using one week of data, we provide statistics regarding the numbers of tweets over time, the hashtags used, and the websites shared. We also illustrate how these data might be utilized by performing an analysis of the prevalence over time of high- and low-credibility sources, topic groups of hashtags, and geographical distributions. Additionally, we develop and present the CoVaxxy dashboard, allowing people to visualize the relationship between COVID-19 vaccine adoption and U.S. geo-located posts in our dataset. This dataset can be used to study the impact of online information on COVID-19 health outcomes (e.g., vaccine uptake) and our dashboard can help with exploration of the data.

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Circulated working papers

• Identification and characterization of misinformation superspreaders on social media

  Abstract

  Matthew R. DeVerna, Rachith Aiyappa, Diogo Pacheco, John Bryden, and Filippo Menczer

  Identification and characterization of misinformation superspreaders on social media

  The world's digital information ecosystem continues to struggle with the spread of misinformation. Prior work has suggested that users who consistently disseminate a disproportionate amount of low-credibility content — so-called superspreaders — are at the center of this problem. We quantitatively confirm this hypothesis and introduce simple metrics to predict the top misinformation superspreaders several months into the future. We then conduct a qualitative review to characterize the most prolific superspreaders and analyze their sharing behaviors. Superspreaders include pundits with large followings, low-credibility media outlets, personal accounts affiliated with those media outlets, and a range of influencers. They are primarily political in nature and use more toxic language than the typical user sharing misinformation. We also find concerning evidence suggesting that Twitter may be overlooking prominent superspreaders. We hope this work will further public understanding of bad actors and promote steps to mitigate their negative impacts on healthy digital discourse.

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Ongoing projects

• ChatGPT fact-checking as a misinformation intervention

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  ChatGPT fact-checking as a misinformation intervention

  Large language models such as ChatGPT have exploded in popularity recently due to their impressive ability on various tasks. In this project, we are conducting a survey experiment that estimates the causal effect of fact-checking information provided by ChatGPT on people's belief in and intention to share political news.

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• Reconstructing social media cascades to identify misinformation amplifiers

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  Reconstructing social media cascades to identify misinformation amplifiers

  Identifying problematic misinformation spreaders on Twitter is a difficult problem because the platform's data assume all retweets are of the original poster, or "originator." Accounts that often reshare content to larger audiences, i.e., "amplifiers," are rendered invisible. This project attempts to address this challenge by presenting a novel approach to reconstruct social media cascades and then utilizing it to investigate the role of amplifiers in disseminating misinformation on Twitter.
  
  We introduce the "Probabilistic Diffusion Inference" (PDI) method which relies on assumed probability distributions to weigh the likelihood of potential parents in a cascade. This approach is stochastic, allowing the generation and analysis of multiple versions of a single cascade, and can flexibly adopt any researcher-formulated probability distribution.

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