Slicing Through the Noise: Exploring Meat Science Conversations on Social Media across the Fake News Spectrum

This study evaluated the conversational landscape of meat science and residues across multiple social media platforms using social monitoring software then explored the connection between fake news, social media, and social monitoring. This thesis follows a two-paper model, where we conducted and present two separate studies utilizing the same message sample collected through social monitoring software, Sprout Social. Guided by Agenda Setting Theory, the authors, purpose, and engagement metrics were recorded through a content analysis to gain a deeper understanding of the conversations about the meat science industry through the eyes of consumers and the information available on social media. After analyzing 343 messages across five social platforms, it was found that many social media users are concerned about how meat affects their health from a nutritional and safety standpoint. Additionally, users from our sample did not provide concrete credentials or hold verification badges that might add credibility to their messages. Metrics, such as reach and engagement, were compared between messages labeled as credible, questionable, suspicious, and fake. A content analysis identified patterns in content, metrics, and authors. We found emotion and sentiment were the strongest indicators of fake news. Further research should be conducted to determine if these patterns are the most reliable across other sectors of the agricultural industry and if instruction to follow the Fake News Checklist helps decrease the impact of fake news on social media users.