142. Content and Linguistic Analysis of COVID-19 Tweets on One University’s Twitter Page

Covid-19 information is commonly shared on Twitter. Universities may use Twitter to share public health information with adolescents and young adults (AYAs). However, the content that universities share regarding Covid-19 on Twitter amid the pandemic is unknown. This study evaluated Covid-19 messaging content from one university’s Twitter account to understand how 1) engagement 2) post frequency and 3) linguistic elements of tweets, changed during the pre-surge and surge phases of fall semester 2020. We conducted a content analysis of the University of Wisconsin’s tweets from August 15 to December 23, 2020 followed by linguistic analysis. First, tweets mentioning Covid-19 or related key words were identified. The number of likes and retweets were recorded and summed to create an engagement score. Second, the post frequency, defined as the total tweets posted during the evaluation period, was recorded to determine a ratio of Covid-19 posts to non Covid-19 posts. Third, the validated Linguistic Inquiry and Word Count (LIWC) program was used to examine tweets. From the LIWC dictionaries we utilized the following analysis categories (example words in parentheses) anxiety (worried, fearful), health (clinic, flu), and reward (praise, benefit). In analyses, tweets were categorized as either pre-surge or surge, based on the Center for Disease Control’s (CDC) designation of the fall Covid-19 surge in Wisconsin (beginning November 1st). T-tests were used to assess significant differences in engagement and LIWC categories during pre-surge and surge times, and the relationship between post frequency and pre-surge and surge times was assessed using a chi square test. A total of 350 tweets were included in this study, and 144 (41.14%) referenced Covid-19. A total of 200 tweets were pre-surge and 150 were surge. We found no significant difference in tweet engagement scores in pre-surge (M= 61.3, SD= 108.3) and surge times (M= 35.5, SD= 68.8, p= .114). We found no significant difference in the frequency of Covid-19 tweets to non-Covid-19 tweets in the pre- and post-surge times (p= .21). Tweets posted in the pre-surge times (M= .37, SD= 1.2) had more anxiety terminology than tweets in the surge times (M= .05, SD= .342, p= .017). Tweets posted in the pre-surge times (M= 1.69, SD= 2.5) had more health terminology than in the surge times (M= .88, SD= 1.7, p= .023). There was more reward terminology in the pre-surge times (M= 2.02, SD= 2.5) than in the surge times (M= 1.13, SD= 1.7, p= .012). Comparatively high health, anxiety, and reward terminology in pre-surge tweets may have reflected an effort to enforce public health protocols when students were returning to campus as Covid-19 cases on campuses increased. However, of all tweets posted by the university in the fall semester, less than half included Covid-19 content, suggesting Covid-19 related information needs to fit into the university’s larger communication plan and social media presence. Future studies should examine the importance of continued social media messaging throughout the semester to promote AYA compliance with public health protocols.