https://orcid.org/0000-0002-0643-2299
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ABSTRACT
Visual content plays a crucial role in today’s online political communication, especially during election campaigns. Prior research on candidate imagery has shown particular effects from non-verbal behavior (e.g., smiling), contextual features (e.g., the depiction of other people), and structural characteristics (e.g., camera angle and proximity). Importantly, this study argues to look at candidate imagery as institutionalized means of political communication online. In the realm of the European Parliamentary Election 2019, self-promoted candidate imagery on social networking sites (SNS) is expected to align cross-nationally along party-family structures vis-à-vis respective imagery in the news, which is anticipated to align along national borders. We analyze and describe respective candidate imagery in both news and SNS from 13,811 unique candidates across all 28 European member states by means of a computational content analysis of 79,500 images. After a manual two-step validation, which raises concerns about the validity of the computationally assigned camera angle, logistic regression models are used to estimate non-verbal behavior, contextual features, and structural characteristics. Findings show that while self-depiction on SNS includes more smiling, news imagery employs broader variation in camera angles and close-up photography. Differences are almost independent from structural influences with the exception of country alignment, which is a minor yet consistent predictor of all outcome variables. Visual computational content analysis has proven to be a useful and reliable utility for all but one variable – a call for its employment along strong validation also in future studies to allow visual content analyses also on a large and quantitative scale.
Acknowledgments
The authors would like to thank the guest editors and the three anonymous reviewers for their very helpful comments. They would also like to thank everyone who has helped to collect national lists of candidates, including Viorela Dan, Mark Debeljak, Daniela Dimitrova, Teresa Maria Federici, Daniel Haim, Jörg Haßler, Emmi Riikonen, Sebastian Scherr, and @Viminale, the Italien Ministry of the Interior’s Twitter team.
Disclosure Statement
No potential conflict of interest was reported by the authors.
Data Availability Statement
The data described in this article are openly available in the Open Science Framework at https://osf.io/wu2z5/.
Open Scholarship
This article has earned the Center for Open Science badges for Open Data and Open Materials through Open Practices Disclosure. The data and materials are openly accessible at https://osf.io/wu2z5/.
Supplementary Material
Supplemental data for this article can be accessed on the publisher’s website at https://doi.org/10.1080/10584609.2020.1753869.
Notes
1. Peng (Citation2018) employed Google’s Image Search. Yet, Google has introduced mechanisms to prevent automated data collection while the script could easily be adapted to Microsoft Bing.
Additional information
Notes on contributors
Mario Haim
Jun.-Prof. Dr. Mario Haim is a professor for Data Journalism at the Institute of Communication and Media Studies at the University of Leipzig. His research focuses on computational journalism, news use within algorithmically curated media environments, and computational social science.
Marc Jungblut
Dr. Marc Jungblut is a postdoctoral researcher at the Department of Media and Communication at LMU Munich. His research focuses on the role of media in conflict, computational social science and strategic communication.