More Like this

1. Getting Local and Personal: Toward Building a Predictive Model for COVID in Three United States Cities.

   Edwards, April; Metcalf, Leigh; Casey, William A; Chatterjee, Shirshendu; Janwa, Heeralal; Battifarano, Ernest (May 2023, Springer)

   Kacprzyk, Janusz; Pal, Nikhil R; Perez, Rafael B; Corchado, Emilio S; Hagras, Hani; Kóczy, László T; Kreinovich, Vladik; Lin, Chin-Teng; Lu, Jie; Melin, Patricia (Ed.)

   The COVID-19 pandemic was lived in real-time on social media. In the current project, we use machine learning to explore the relationship between COVID-19 cases and social media activity on Twitter. We were particularly interested in determining if Twitter activity can be used to predict COVID-19 surges. We also were interested in exploring features of social media, such as replies, to determine their promise for understanding the views of individual users. With the prevalence of mis/disinformation on social media, it is critical to develop a deeper and richer understanding of the relationship between social media and real-world events in order to detect and prevent future influence operations. In the current work, we explore the relationship between COVID-19 cases and social media activity (on Twitter) in three major United States cities with different geographical and political landscapes. We find that Twitter activity resulted in statistically significant correlations using the Granger causality test, with a lag of one week in all three cities. Similarly, the use of replies, which appear more likely to be generated by individual users, not bots or public relations operations, was also strongly correlated with the number of COVID-19 cases using the Granger causality test. Furthermore, we were able to build promising predictive models for the number of future COVID-19 cases using correlation data to select features for input to our models. In contrast, significant correlations were not identified when comparing the number of COVID-19 cases with mainstream media sources or with a sample of all US COVID-related tweets. We conclude that, even for an international event such as COVID-19, social media tracks closely with local conditions. We also suggest that replies can be a valuable feature within a machine learning task that is attempting to gauge the reactions of individual users. 

   more » « less
2. Bots and Misinformation Spread on Social Media: Implications for COVID-19

   https://doi.org/10.2196/26933  

   Himelein-Wachowiak, McKenzie; Giorgi, Salvatore; Devoto, Amanda; Rahman, Muhammad; Ungar, Lyle; Schwartz, H Andrew; Epstein, David H; Leggio, Lorenzo; Curtis, Brenda (January 2021, Journal of Medical Internet Research)

   As of March 2021, the SARS-CoV-2 virus has been responsible for over 115 million cases of COVID-19 worldwide, resulting in over 2.5 million deaths. As the virus spread exponentially, so did its media coverage, resulting in a proliferation of conflicting information on social media platforms—a so-called “infodemic.” In this viewpoint, we survey past literature investigating the role of automated accounts, or “bots,” in spreading such misinformation, drawing connections to the COVID-19 pandemic. We also review strategies used by bots to spread (mis)information and examine the potential origins of bots. We conclude by conducting and presenting a secondary analysis of data sets of known bots in which we find that up to 66% of bots are discussing COVID-19. The proliferation of COVID-19 (mis)information by bots, coupled with human susceptibility to believing and sharing misinformation, may well impact the course of the pandemic. 

   more » « less
3. Supporting Individual Risk Assessment during COVID-19 (2022)

   https://doi.org/10.17226/26629  

   Fischhoff, Baruch; Joslyn, Susan; Limaye, Rupali; Murphy, Sheila T. (June 2022, Publications listing National Academy of Sciences National Academy of Engineering Institute of Medicine National Research Council)

   Many individual states and localities have loosened or eliminated mitigation measures as the COVID-19 pandemic has evolved. Individuals who wish to mitigate their risk of contracting COVID-19 must decide on effective strategies in environments that may have returned to pre-pandemic norms. Individuals must assess their level of risk and risk tolerance amid different mitigation measures, regulations, and metrics across states and localities. The public is also exposed to misinformation and disinformation through social and mainstream media—all occurring within a politically polarized environment. 

   more » « less
4. Online hate network spreads malicious COVID-19 content outside the control of individual social media platforms

   https://doi.org/10.1038/s41598-021-89467-y  

   Velásquez, N.; Leahy, R.; Restrepo, N. Johnson; Lupu, Y.; Sear, R.; Gabriel, N.; Jha, O. K.; Goldberg, B.; Johnson, N. F. (June 2021, Scientific Reports)

   Abstract We show that malicious COVID-19 content, including racism, disinformation, and misinformation, exploits the multiverse of online hate to spread quickly beyond the control of any individual social media platform. We provide a first mapping of the online hate network across six major social media platforms. We demonstrate how malicious content can travel across this network in ways that subvert platform moderation efforts. Machine learning topic analysis shows quantitatively how online hate communities are sharpening COVID-19 as a weapon, with topics evolving rapidly and content becoming increasingly coherent. Based on mathematical modeling, we provide predictions of how changes to content moderation policies can slow the spread of malicious content. 

   more » « less
5. Twitter and Facebook posts about COVID-19 are less likely to spread misinformation compared to other health topics

   https://doi.org/10.1371/journal.pone.0261768  

   Broniatowski, David A.; Kerchner, Daniel; Farooq, Fouzia; Huang, Xiaolei; Jamison, Amelia M.; Dredze, Mark; Quinn, Sandra Crouse; Ayers, John W. (January 2022, PLOS ONE)

   Guidi, Barbara (Ed.)

   The COVID-19 pandemic brought widespread attention to an “infodemic” of potential health misinformation. This claim has not been assessed based on evidence. We evaluated if health misinformation became more common during the pandemic. We gathered about 325 million posts sharing URLs from Twitter and Facebook during the beginning of the pandemic (March 8-May 1, 2020) compared to the same period in 2019. We relied on source credibility as an accepted proxy for misinformation across this database. Human annotators also coded a subsample of 3000 posts with URLs for misinformation. Posts about COVID-19 were 0.37 times as likely to link to “not credible” sources and 1.13 times more likely to link to “more credible” sources than prior to the pandemic. Posts linking to “not credible” sources were 3.67 times more likely to include misinformation compared to posts from “more credible” sources. Thus, during the earliest stages of the pandemic, when claims of an infodemic emerged, social media contained proportionally less misinformation than expected based on the prior year. Our results suggest that widespread health misinformation is not unique to COVID-19. Rather, it is a systemic feature of online health communication that can adversely impact public health behaviors and must therefore be addressed. 

   more » « less