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Many disciplines, including the broad Field of Information (iField), have been offering Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate-level and undergraduate-level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context.
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Background: Public health agencies widely adopt social media for health and risk communication. Moreover, different platforms have different affordances, which may impact the quality and nature of the messaging and how the public engages with the content. However, these platform effects are not often compared in studies of health and risk communication and not previously for the COVID-19 pandemic. Objective: This study measures the potential media effects of Twitter and Facebook on public health message design and engagement by comparing message elements and audience engagement in COVID-19-related posts by local, state, and federal public health agencies in the United States during the pandemic, to advance theories of public health messaging on social media and provide recommendations for tailored social media communication strategies. Methods: We retrieved all COVID-19-related posts from major US federal agencies related to health and infectious disease, all major state public health agencies, and selected local public health departments on Twitter and Facebook. A total of 100,785 posts related to COVID-19, from 179 different accounts of 96 agencies, were retrieved for the entire year of 2020. We adopted a framework of social media message elements to analyze the posts across Facebook and Twitter. For manual content analysis, we subsampled 1677 posts. We calculated the prevalence of various message elements across the platforms and assessed the statistical significance of differences. We also calculated and assessed the association between message elements with normalized measures of shares and likes for both Facebook and Twitter. Results: Distributions of message elements were largely similar across both sites. However, political figures (P<.001), experts (P=.01), and nonpolitical personalities (P=.01) were significantly more present on Facebook posts compared to Twitter. Infographics (P<.001), surveillance information (P<.001), and certain multimedia elements (eg, hyperlinks, P<.001) were more prevalent on Twitter. In general, Facebook posts received more (normalized) likes (0.19%) and (normalized) shares (0.22%) compared to Twitter likes (0.08%) and shares (0.05%). Elements with greater engagement on Facebook included expressives and collectives, whereas posts related to policy were more engaged with on Twitter. Science information (eg, scientific explanations) comprised 8.5% (73/851) of Facebook and 9.4% (78/826) of Twitter posts. Correctives of misinformation only appeared in 1.2% (11/851) of Facebook and 1.4% (12/826) of Twitter posts. Conclusions: In general, we find a data and policy orientation for Twitter messages and users and a local and personal orientation for Facebook, although also many similarities across platforms. Message elements that impact engagement are similar across platforms but with some notable distinctions. This study provides novel evidence for differences in COVID-19 public health messaging across social media sites, advancing knowledge of public health communication on social media and recommendations for health and risk communication strategies on these online platforms.
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Background: Since COVID-19 vaccines became broadly available to the adult population, sharp divergences in uptake have emerged along partisan lines. Researchers have indicated a polarized social media presence contributing to the spread of mis- or disinformation as being responsible for these growing partisan gaps in uptake. Objective: The major aim of this study was to investigate the role of influential actors in the context of the community structures and discourse related to COVID-19 vaccine conversations on Twitter that emerged prior to the vaccine rollout to the general population and discuss implications for vaccine promotion and policy. Methods: We collected tweets on COVID-19 between July 1, 2020, and July 31, 2020, a time when attitudes toward the vaccines were forming but before the vaccines were widely available to the public. Using network analysis, we identified different naturally emerging Twitter communities based on their internal information sharing. A PageRank algorithm was used to quantitively measure the level of "influentialness" of Twitter accounts and identifying the "influencers," followed by coding them into different actor categories. Inductive coding was conducted to describe discourses shared in each of the 7 communities. Results: Twitter vaccine conversations were highly polarized, with different actors occupying separate "clusters." The antivaccine cluster was the most densely connected group. Among the 100 most influential actors, medical experts were outnumbered both by partisan actors and by activist vaccine skeptics or conspiracy theorists. Scientists and medical actors were largely absent from the conservative network, and antivaccine sentiment was especially salient among actors on the political right. Conversations related to COVID-19 vaccines were highly polarized along partisan lines, with "trust" in vaccines being manipulated to the political advantage of partisan actors. Conclusions: These findings are informative for designing improved vaccine information communication strategies to be delivered on social media especially by incorporating influential actors. Although polarization and echo chamber effect are not new in political conversations in social media, it was concerning to observe these in health conversations on COVID-19 vaccines during the vaccine development process.
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The circulation of myriad of information from diverse digital platforms during the COVID-19 pandemic caused the unprecedented infodemic. Along with the increased case numbers, the shared information accelerated exponentially, especially via social media, and a large proportion of the daily distributed information was blended with myth, rumors, pseudoscience, or modified facts. Uncovering viral mis- and disinformation narratives and information voids is essential to a swift and effective response on delivering public health information and policy by the governments during a public health emergency. Although many studies have examined how information was circulated and shared during the COVID-19 pandemic era, large gaps in literature exist as to how effectively to track, describe, and answer it. In this panel, the panelists propose and discuss data science methods to analyze the COVID-19 infodemic. We hope our panel contribute to exploring more effective and applicable data science methods to investigate infodemic in crises.
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During the COVID-19 pandemic, antivaccination social media accounts are proliferating online, threatening to further escalate vaccine hesitancy related to the COVID-19 vaccine. This commentary seeks to alert and encourage the health care provider community, including health care professionals and academic organizations, to engage in social media to counter the mounting vaccine-related infodemic. To validate our recommendation for engagement, the authors describe preliminary findings using a mixed methods approach of quantitative Twitter-based ranking algorithms of networks and users with qualitative content analysis of 1 million tweets related to COVID-19 vaccine conversations. Results show highly polarized and active antivaccine conversations that were primarily influenced by political and nonmedical Twitter users. In contrast, less than 10% of the tweets stemmed from the medical community, demonstrating a lack of active health care professional connectivity in addressing COVID-19 misinformation. The authors introduce the concept of Health Care Provider Social Media Hesitancy to refer to the public health threat of health care providers' nonaction in providing pro-vaccine and scientific information about the vaccine on social media. The authors conclude by describing multilevel strategies for encouraging health care providers and the medical community to effectively "Tweet up" to combat the mounting threat of vaccine misinformation and hesitancy.
