Rethinking Social Amplification of Risk: Social Media and Zika in Three Languages.

Autor: Wirz CD; Department of Life Sciences Communication, University of Wisconsin-Madison, Madison, WI, USA., Xenos MA; Department of Life Sciences Communication, University of Wisconsin-Madison, Madison, WI, USA., Brossard D; Department of Life Sciences Communication, University of Wisconsin-Madison, Madison, WI, USA.; Morgridge Institute for Research, Madison, WI, USA., Scheufele D; Department of Life Sciences Communication, University of Wisconsin-Madison, Madison, WI, USA.; Morgridge Institute for Research, Madison, WI, USA., Chung JH; Department of Life Sciences Communication, University of Wisconsin-Madison, Madison, WI, USA., Massarani L; National Institute of Public Communication of Science and Technology and Master of Communication of Science, Technology and Health, Fiocruz, Rio de Janeiro, Brazil.
Jazyk: angličtina
Zdroj: Risk analysis : an official publication of the Society for Risk Analysis [Risk Anal] 2018 Dec; Vol. 38 (12), pp. 2599-2624. Date of Electronic Publication: 2018 Nov 08.
DOI: 10.1111/risa.13228
Abstrakt: Using the Zika outbreak as a context of inquiry, this study examines how assigning blame on social media relates to the social amplification of risk framework (SARF). Past research has discussed the relationship between the SARF and traditional mass media, but the role of social media platforms in amplification or attenuation of risk perceptions remains understudied. Moreover, the communication and perceptions of Zika-related risk are not limited to discussions in English. To capture conversations in languages spoken by affected countries, this study combines data in English, Spanish, and Portuguese. To better understand the assignment of blame and perceptions of risk in new media environments, we looked at three different facets of conversations surrounding Zika on Facebook and Twitter: the prominence of blame in each language, how specific groups were discussed throughout the Zika outbreak, and the sentiment expressed about genetically engineered (GE) mosquitoes. We combined machine learning with human coding to analyze public discourse in all three languages. We found differences between languages and platforms in the amount of blame assigned to different groups. We also found more negative sentiments expressed about GE mosquitoes on Facebook than on Twitter. These meaningful differences only emerge from analyses across the three different languages and platforms, pointing to the importance of multilingual approaches for risk communication research. Specific recommendations for outbreak and risk communication practitioners are also discussed.
(© 2018 Society for Risk Analysis.)
Databáze: MEDLINE
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