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1. “I Heard That COVID-19 Was...”: Rumors, Pandemic, and Psychological Distance

   https://doi.org/10.1177/00027642211066026  

   Kwon, K. Hazel; Pellizzaro, Kirstin; Shao, Chun; Chadha, Monica (January 2022, American Behavioral Scientist)

   The spread of misinformation through a variety of communication channels has amplified society’s challenge to manage the COVID-19 pandemic. While existing studies have examined how misinformation spreads, few studies have examined the role of psychological distance in people’s mental processing of a rumor and their propensity to accept self-transformed narratives of the message. Based on an open-ended survey data collected in the U.S. ( N = 621) during an early phase of the pandemic, the current study examines how psychological distance relates to the transformation and acceptance of conspiratorial narratives in the context of the COVID-19 pandemic. Two instances of misinformation are examined, both of which were widely heard at the time of data collection: the role of (a) Bill Gates and (b) government during the outbreak of the pandemic. This study uses topic modeling techniques to capture distinctive topical attributes that emerged from rumor narratives. In addition, statistical analyses estimate the psychological distance effects on the salience of topical attributes of a rumor story and an individual’s propensity to believe them. Findings reveal that psychological distance to the threats of COVID-19 influences how misinformation evolves through word-of-mouth, particularly in terms of who is responsible for the pandemic and why the world finds itself in the current situation. Psychological distance also explains why people accept the message to be true. Implications for misinformation and rumor psychology research, as well as avenues for future research, are discussed. 

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2. The Impact of COVID-19 on Cybersecurity

   Armstrong, Ayanna; Boonthum-Denecke, Chutima (January 2023, The 2023 ADMI Symposium)

   This report will discuss the impact of COVID-19 on cybersecurity. This report also discusses cyber threats in the home office, the healthcare and public health (HPH) sector in attackers’ sight, and the rise of COVID-themed phishing attacks targeted towards remote workers and internet users. Tips on mitigating cyber risk will also be touched upon. This study utilizes surveys conducted to identify the effects this ongoing pandemic has had on businesses and people. 

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3. An Analysis of Temporal Trends in Anti-Asian Hate and Counter-Hate on Twitter During the COVID-19 Pandemic

   https://doi.org/10.1089/cyber.2022.0206  

   Wheeler, Brittany; Jung, Seong; Hall, Deborah L.; Purohit, Monika; Silva, Yasin (July 2023, Cyberpsychology, Behavior, and Social Networking)

   Recent studies have documented increases in anti-Asian hate throughout the COVID-19 pandemic. Yet relatively little is known about how anti-Asian content on social media, as well as positive messages to combat the hate, have varied over time. In this study, we investigated temporal changes in the frequency of anti-Asian and counter-hate messages on Twitter during the first 16 months of the COVID-19 pandemic. Using the Twitter Data Collection Application Programming Interface, we queried all tweets from January 30, 2020 to April 30, 2021 that contained specific anti-Asian (e.g., #chinavirus, #kungflu) and counter-hate (e.g., #hateisavirus) keywords. From this initial data set, we extracted a random subset of 1,000 Twitter users who had used one or more anti-Asian or counter-hate keywords. For each of these users, we calculated the total number of anti-Asian and counter-hate keywords posted each month. Latent growth curve analysis revealed that the frequency of anti-Asian keywords fluctuated over time in a curvilinear pattern, increasing steadily in the early months and then decreasing in the later months of our data collection. In contrast, the frequency of counter-hate keywords remained low for several months and then increased in a linear manner. Significant between-user variability in both anti-Asian and counter-hate content was observed, highlighting individual differences in the generation of hate and counter-hate messages within our sample. Together, these findings begin to shed light on longitudinal patterns of hate and counter-hate on social media during the COVID-19 pandemic. 

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4. From rumor to genetic mutation detection with explanations: a GAN approach

   https://doi.org/10.1038/s41598-021-84993-1  

   Cheng, Mingxi; Li, Yizhi; Nazarian, Shahin; Bogdan, Paul (December 2021, Scientific Reports)

   null (Ed.)

   Abstract Social media have emerged as increasingly popular means and environments for information gathering and propagation. This vigorous growth of social media contributed not only to a pandemic (fast-spreading and far-reaching) of rumors and misinformation, but also to an urgent need for text-based rumor detection strategies. To speed up the detection of misinformation, traditional rumor detection methods based on hand-crafted feature selection need to be replaced by automatic artificial intelligence (AI) approaches. AI decision making systems require to provide explanations in order to assure users of their trustworthiness. Inspired by the thriving development of generative adversarial networks (GANs) on text applications, we propose a GAN-based layered model for rumor detection with explanations. To demonstrate the universality of the proposed approach, we demonstrate its benefits on a gene classification with mutation detection case study. Similarly to the rumor detection, the gene classification can also be formulated as a text-based classification problem. Unlike fake news detection that needs a previously collected verified news database, our model provides explanations in rumor detection based on tweet-level texts only without referring to a verified news database. The layered structure of both generative and discriminative models contributes to the outstanding performance. The layered generators produce rumors by intelligently inserting controversial information in non-rumors, and force the layered discriminators to detect detailed glitches and deduce exactly which parts in the sentence are problematic. On average, in the rumor detection task, our proposed model outperforms state-of-the-art baselines on PHEME dataset by $$26.85\%$$ 26.85 % in terms of macro-f1. The excellent performance of our model for textural sequences is also demonstrated by the gene mutation case study on which it achieves $$72.69\%$$ 72.69 % macro-f1 score. 

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5. Politicization of a Pathogen: A Prospective Longitudinal Study of COVID ‐19 Responses in a Nationally Representative U.S. Sample

   https://doi.org/10.1111/pops.12894  

   Relihan, Daniel P.; Holman, E. Alison; Garfin, Dana Rose; Ditto, Peter H.; Silver, Roxane Cohen (May 2023, Political Psychology)

   Understanding population‐level variability in responses to pathogens over time is important for developing effective health‐based messages targeted at ideologically diverse populations. Research from psychological and political sciences suggests that party and elite cues shape how people respond to major threats like climate change. Research on responses to the COVID‐19 pandemic suggests similar variability across party identities; however, prior work has methodological limitations. This prospective, longitudinal study of a large probability‐based nationally representative U.S. sample assessed in March–April 2020 (N = 6,514) and then 6 months later in September–October 2020 (N = 5,661) demonstrates that COVID‐19 fear, perceived COVID‐19 death risk, and reported health‐protective behaviors became increasingly polarized over the first 6 months of the pandemic. Initial differences between Democrats and Republicans failed to converge over time and became more pronounced. Responses among Republicans were further polarized by support for former President Donald Trump: Trump Republicans initially reported weaker responses to COVID‐19 than non‐Trump Republicans, and these differences became more pronounced over time. Importantly, political identity and Trump support were not linked to perceived infection risk of a nonpoliticized pathogen, the flu. Finally, political identity and Republican Trump support prospectively predicted COVID‐19 vaccine intentions 6 months into the pandemic. 

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