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The purpose of this study is to explore the role of data visualization in the media coverage of the 2016 presidential election in the United States focusing on data-visualization projects from the New York Times and the Washington Post. This research aims to examine how media coverage of the election has been framed with infographics through mixed methods analysis. Another part of this article presents an investigation of data journalists' working routines and so-called boundary-work (Boyles, J. L., and E. Meyer. 2016. "Letting the Data Speak." Digital Journalism 4 (7): 944–954. doi:10.1080/21670811.2016.1166063) by conducting interviews with data reporters. The mixed methods textual and visual analysis shows that framing the election as a game is still relevant for the election data reporting. Analysis of graphics before the election has demonstrated the prevalent game frame trend in data visualization pieces. However, this pattern has changed after the election with increased priority for the issue frame and policy coverage. Results have demonstrated that journalists tend to reinforce the game frame in the infographics. That phenomenon leads to a potential increase in political polarization and political skepticism. Analysis of the interviews with data journalists from two newsrooms shows that professional work routines affect their reporting and reinforce the detrimental influence of framing on the election coverage. [ABSTRACT FROM AUTHOR]

The recent COVID-19 outbreak has highlighted the importance of effective communication strategies to control the spread of the virus and debunk misinformation. By using accurate narratives, both online and offline, we can motivate communities to follow preventive measures and shape attitudes toward them. However, the abundance of misinformation stories can lead to vaccine hesitancy, obstructing the timely implementation of preventive measures, such as vaccination. Therefore, it is crucial to create appropriate and community-centered solutions based on regional data analysis to address mis/disinformation narratives and implement effective countermeasures specific to the particular geographic area. In this case study, we have attempted to create a research pipeline to analyze local narratives on social media, particularly Twitter, to identify misinformation spread locally, using the state of Pennsylvania as an example. Our proposed methodology pipeline identifies main communication trends and misinformation stories for the major cities and counties in southwestern PA, aiming to assist local health officials and public health specialists in instantly addressing pandemic communication issues, including misinformation narratives. Additionally, we investigated anti-vax actors' strategies in promoting harmful narratives. Our pipeline includes data collection, Twitter influencer analysis, Louvain clustering, BEND maneuver analysis, bot identification, and vaccine stance detection. Public health organizations and community-centered entities can implement this data-driven approach to health communication to inform their pandemic strategies. [ABSTRACT FROM AUTHOR]