Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Social and Behavioral Sciences
U.S. Geographic Differences In Media Source Use During Covid-19 Shelter In Place Orders, Allison R. Fortner, Kristin Gibson, Alexa Lamm
U.S. Geographic Differences In Media Source Use During Covid-19 Shelter In Place Orders, Allison R. Fortner, Kristin Gibson, Alexa Lamm
Journal of Applied Communications
United States news access patterns may have influenced distribution of misinformation in the COVID-19 infodemic, emphasizing the necessity of targeted communication to increase health literacy during a crisis. This study used sense-making theory to explore information-seeking behaviors of U.S. residents during COVID-19 shelter in place orders. This purpose of this study was to identify media outlets used by U.S. residents to access COVID-19 information and determine if access differed according to geographic region. A representative survey of U.S. residents aged 18 or older (N = 1,048) revealed the mainstream media outlets used most were domestic government-based sources. Northeastern …
Emotion And Virality Of Food Safety Risk Communication Messages On Social Media, Xiaojing (Romy) Wang, Xiaoli Nan, Samantha J. Stanley, Yuan Wang, Leah Waks, David Broniatowski
Emotion And Virality Of Food Safety Risk Communication Messages On Social Media, Xiaojing (Romy) Wang, Xiaoli Nan, Samantha J. Stanley, Yuan Wang, Leah Waks, David Broniatowski
Journal of Applied Communications
This study investigates how the emotional tone of food safety risk communication messages predicts message virality on social media. Through a professional Internet content tracking service, we gathered news articles written about the 2018 romaine lettuce recall published online between October 30th and November 29th, 2018. We retrieved the number of times each article was shared on Twitter and Pinterest, and the number of engagements (shares, likes, and comments) for each article on Facebook and Reddit. We randomly selected 10% of the articles (n = 377) and characterized the emotional tone of each article using machine learning, …