- Dr. Leona Yi-Fan Su, Assistant Professor, Dept. of Advertising
- Dr. Margaret Yee Man Ng, Assistant Professor, Journalism
- Dr. Yi-Cheng Wang, Assistant Professor, Dept. of Food Science & Human Nutrition
Public discourse about novel technologies has the potential to become divisive in ways that could compromise their responsible development. Recently, scientists have been able to produce meat through in vitro cultivation of animal cells, and are proposing the resultant cultured meat as a potential solution to various problems facing conventional livestock production. However, this technology is raising stakeholders’ regulatory and risk concerns, much as other novel biotechnologies have in the past; and societal debates of this kind have often led to public behavior at odds with what scientists and science communicators are aiming for.
To forestall such future battles and inform policy making, an understanding of the emerging frames and sentiments around this food technology and their impact on attitude formation is required urgently. Data mining of social media platforms such as Twitter for discussions about cultured meat offers an unprecedented opportunity to fill this gap. Specifically, this project’s two objectives are: (1) to use a hybrid lexical and machine learning-based system to perform thematic and sentiment analysis of cultured meat-focused tweets, and delineate the links among thematic frames, sentiments, technology labeling (e.g., “cultured meat,” “lab-grown meat”), and stakeholder groups; and (2) to conduct a pilot online survey experiment testing how people’s attitudes toward cultured meat are affected by different labelings and their associated frames and sentiments, as identified via thematic and sentiment analysis.
As well as contributing to nascent debates around cultured meat, and helping to ensure that policy and consumer choices are guided not only by the best available science but also by citizens’ values and concerns, this project is expected to yield methodological advancements in the mining of social media data to inform our understanding of the wider dynamics of public communication. The preliminary data will also form the basis of further proposals to the USDA and NSF.