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

Christopher D Wirz, Michael A Xenos, Dominique Brossard, Dietram Scheufele, Jennifer H Chung, Luisa Massarani
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引用次数: 62

Abstract

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.

重新思考风险的社会放大:三种语言的社交媒体和寨卡病毒。
本研究以寨卡疫情为调查背景,探讨了将责任归咎于社交媒体与风险社会放大框架(SARF)之间的关系。过去的研究已经讨论了SARF与传统大众媒体之间的关系,但社交媒体平台在放大或减弱风险感知方面的作用仍未得到充分研究。此外,对寨卡病毒相关风险的沟通和认知并不局限于用英语进行讨论。为了捕捉受影响国家所说语言的对话,本研究结合了英语、西班牙语和葡萄牙语的数据。为了更好地理解新媒体环境下的责任分配和风险感知,我们研究了Facebook和Twitter上围绕寨卡病毒的对话的三个不同方面:每种语言中指责的突出程度,寨卡病毒爆发期间特定群体的讨论方式,以及对基因工程蚊子的表达情绪。我们将机器学习与人类编码结合起来,分析了这三种语言的公共话语。我们发现不同语言和平台对不同群体的指责是不同的。我们还发现,Facebook上对转基因蚊子的负面情绪比Twitter上更多。这些有意义的差异只有在跨三种不同语言和平台的分析中才会出现,这表明多语言方法对风险沟通研究的重要性。还讨论了针对疫情和风险沟通从业人员的具体建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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