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This study examined public comments on Twitter in the wake of three mass shootings in the United States during the summer of 2022. A total of 1,500 tweets were assessed (N = 1,500) for sentiment, risks presented, attribution of blame, and outrage. A sample of 500 tweets was taken following the Buffalo, New York, supermarket shooting; a sample of 500 tweets was taken following the Uvalde, Texas, school shooting; and a sample of 500 tweets was taken following the Highland Park, Illinois, Fourth of July parade shooting. Results show that risk, blame, and outrage differed significantly between the three situations in a variety of ways. These differences and possible reasons for them are further discussed. This study provides valuable insight on social media conversations about mass shootings – a timely subject that continues to plague the country and lead to polarizing opinions and divisiveness. The study also serves a practical function, as the findings can be presented to government organizations and social media organizations alike as critical insight into public perceptions, it can assist in identifying problematic speech online, and it can inform the development of social media guidelines in maintaining civil discord. [ABSTRACT FROM AUTHOR]

This project explored perceptions of ChatGPT in higher education among students and faculty to assess teaching and learning implications of this Generative Artificial Intelligence (Generative AI)–based novel tool. Two theoretical frameworks inspired the project, including Diffusion of Innovation theory (Rogers, 1962) and Technology Acceptance Model (Davis, 1989). An online survey was completed by 380 participants (N = 380). Participants indicated that they would not use ChatGPT to plagiarize but believed others would. When asked to rate the accuracy of ChatGPT's output, more than half took the incorrect output as correct/somewhat correct or could not tell whether it was correct or incorrect. Results varied based on participant demographics, including age, gender, and occupation. These findings support the need for data literacy. If Generative AI is to be used in higher education to aid in the learning process, it is imperative to continue teaching critical thinking. [ABSTRACT FROM AUTHOR]

Objective: This project assessed college students' perceptions of social media conversations about COVID-19 in the United States. Participants: A total of 110 college students completed an online survey (N = 110) over two periods of time: April 2020 (n = 60) and September 2020 (n = 50). Method: Measurements included students' perceptions of sentiment, risk, blame, outrage, and opinion/behavioral intention. Results: Survey results showed that college students' attitudes and perceptions of these concepts on social media varied from April to September with negative sentiment, concern, uncertainty, and risk perception decreasing over time while blame and outrage increased over time. A sample of tweets was also examined for these constructs. Conclusions: This study provides important insight on college students' perceptions during a public health emergency and how they use social media as a tool for health information. [ABSTRACT FROM AUTHOR]

Background: This study examined public comments on Twitter about vaping in the days following national news stories of the 'vaping-related illness epidemic.' What began in August 2019 with eight hospitalizations in Wisconsin spiraled into an 'epidemic' linked to at least 12 deaths (Carlisle M. (2019, September 17). Seventh person to die from vaping-related illness in U.S. dies in California. Yahoo! Lifestyle. Available from: ; Haglage A. (2019, September 26). CDC reports 52% increase in vaping-relating illnesses amid shake-up at Juul. Yahoo! Finance. Available from: ; Kaplan, S. & Richtel, M. (2019, September 11). The mysterious vaping illness that's 'becoming an epidemic.' New York Times. Available from: ). Method: A total of 500 tweets were assessed (N = 500) for sentiment, health claims, risks presented, and outrage. Specifically, four categories adapted from [1] were used to categorize risk and outrage. Results: It was found that the sampled tweets were very much against the regulation of vaping and e-cigarettes. More than 36 percent were negative toward regulations and only 2 percent were positive toward regulations. More than 55 percent of the tweets examined contained no risk mentioned at all. The primary area of concern or outrage was related to the potential for regulation, not about the health risk. Conclusions: These results were interesting in that little risk was noted by tweeters as compared to the risks being promoted in mass media. This study is an important contribution to a burgeoning area of research. It also serves a practical function, as the findings can be presented to government organizations as invaluable information regarding audience insights and the development of anti-vaping campaigns. [ABSTRACT FROM AUTHOR]