In a rapidly changing public health crisis such as COVID-19, researchers need innovative approaches that can effectively link qualitative approaches and computational methods. In this article, computational and qualitative methods are used to analyze survey data collected in March 2020 ( n = 2,270) to explore the content of persuasive messages and their relationship with self-reported health behavior—that is, social distancing. Results suggest that persuasive messages, based on participants’ perspectives, vary by gender and race and are associated with self-reported health behavior. This article illustrates how qualitative analysis and structural topic modeling can be used in synergy in a public health study to understand the public’s perception and behavior related to science issues. Implications for health communication and future research are discussed.
The idea of faculty engaging in meaningful dialogue with different publics instead of simply communicating their research to interested audiences has gradually morphed from a novel concept to a mainstay within most parts of the academy. Given the wide variety of public engagement modalities, it may be unsurprising that we still lack a comprehensive and granular understanding of factors that influence faculty willingness to engage with public audiences. Those nuances are not always captured by quantitative surveys that rely on pre-determined categories to assess scholars’ willingness to engage. While closed-ended categories are useful to examine which factors influence the willingness to engage more than others, it is unlikely that pre-determined categories comprehensively represent the range of factors that undermine or encourage engagement, including perceptual influences, institutional barriers, and scholars’ lived experiences. To gain insight into these individual perspectives and lived experiences, we conducted focus group discussions with faculty members at a large midwestern land-grant university in the United States. Our findings provide context to previous studies of public engagement and suggest four themes for future research. These themes affirm the persistence of institutional barriers to engaging with the public, particularly the expectations in the promotion process for tenure-track faculty. However, we also find a perception that junior faculty and graduate students are challenging the status quo by introducing a new wave of attention to public engagement. This finding suggests a “trickle-up” effect through junior faculty and graduate students expecting institutional support for public engagement. Our findings highlight the need to consider how both top-down factors such as institutional expectations and bottom-up factors such as graduate student interest shape faculty members’ decisions to participate in public engagement activities.
Understanding how individuals perceive the barriers and benefits of precautionary actions is key for effective communication about public health crises, such as the COVID-19 outbreak. This study used innovative computational methods to analyze 30,000 open-ended responses from a large-scale survey to track how Wisconsin (U.S.A.) residents' perceptions of the benefits of and barriers to performing social distancing evolved over a critical time period (March 19th to April 1st, 2020). Initially, the main barrier was practical related, however, individuals later perceived more multifaceted barriers to social distancing. Communication about COVID-19 should be dynamic and evolve to address people's experiences and needs overtime.
The use of artificial intelligence-based algorithms for the curation of news content by social media platforms like Facebook and Twitter has upended the gatekeeping role long held by traditional news outlets. This has caused some US policymakers to argue that platforms are skewing news diets against them, and such claims are beginning to take hold among some voters. In a nationally representative survey experiment, we explore whether traditional models of media bias perceptions extend to beliefs about algorithmic news bias. We find that partisan cues effectively shape individuals’ attitudes about algorithmic news bias but have asymmetrical effects. Specifically, whereas in-group directional partisan cues stimulate bias perceptions for members of both parties, Democrats, but not Republicans, also respond to out-group cues. We conclude with a discussion about the implications for the formation of attitudes about new technologies and the potential for polarization.
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