Preliminary Conference Agenda

This paper reports on an in-progress study analyzing youth technology experiences through a collection of stories created and openly licensed by students. We analyzed the transcripts of student-created animated video stories for a student media lab-based project in a social media studies course in spring 2021. Open coding of 44 transcripts found that students reflect on their past social media experiences through key thematic heuristics, such as contexts of adoption including grade level, mood, and influence; and dimensions of growing self-awareness around use including influences of others, changes in popular platforms like Instagram, and changes from playful to curated self-presentation. We present early analysis of code co-occurrences including emotion and influence, grade level and influence, and emotional weight specifically around Instagram. We end with plans for further research on this and related datasets, including audiovisual data and analysis through the lens of media literacy, and implications for researchers and instructors in information, new media, and education.

Abstract. As social media such as Twitter has become an important medium for disseminating information, it is essential to understand how the information diffusion on social media influences public adoption of vaccines. Based on the innovation diffusion theory, we construct a user and information quality indicator system for early adopters of COVID-19 vaccination by identifying their creation of user-generated content on social media. Machine learning approaches and text analysis methods are used to perform topic clustering and sentiment analysis on vaccination-related tweets on Twitter. Based on each country’s vaccination data in January 2021, the study examines the relationship between the quality of social media early adopters, and the quality of the information they publish with vaccine adoption by using the OSL regression model. The empirical results show that the total number of tests, the number of new COVID-19 cases, and the human development index have a significantly positive influence on vaccine adoption. Neutral emotions and offensive language of early adopters on social media have a significantly negative relationship with vaccine adoption. These interesting findings can help governments and public health officials understand early adopters' perceptions of vaccines, and play an important role in targeted policy interventions.

Social networks currently serve not only as platforms for pub-lishing content but also as fundamental tools for accessing information. This role in providing access is mediated by a series of opaque, algorithm-based mechanisms for personalising the content. This article draws on ex-isting literature on the relationship between possible mental health disor-ders and the functioning of these platforms to try to understand their effects on elements such as self-esteem and emotions. To this end, it focuses on the Instagram social network, which is prominent in the user groups corre-sponding to the Millennial and Z generations due to its high visual and multimedia content, its capacity for uncovering trends, and its integration with social commerce. It presents the results of a study (n=100) of Insta-gram users between the ages of 18 and 39. These results provide relevant da-ta on patterns associated with the following: time spent on the platform and excessive use, the risk of emotional loneliness or isolation, displacement of daily activities, and feelings of inferiority. They also reveal a real lack of awareness of how the algorithms on these types of platforms work and an interest in the mechanisms of disconnection and digital well-being. Lastly, the results open up new possibilities for inclusion of these risks in digital literacy programmes.