Video-sharing platforms such as YouTube, TikTok, and Twitch saw a rapid increase in popularity and affected the public socially and emotionally. Besides entertainment, users build connections, seek help, and enjoy visual-audio experiences on YouTube. Creators frequently upload videos and interact with the viewers, making YouTube a virtual place to mitigate loneliness, manage mental health issues, and obtain relaxation and intimacy. Researchers need to conceptualize the roles of user-generated videos in delivering social-emotional information, support, and experiences. In this talk, I share my analyses of YouTube videos on topics of COVID-19 loneliness, drug addiction, and the popular ASMR (Autonomous Sensory Meridian Response) experiences. These studies explained recent YouTube trends and exemplified how YouTubers act as a community to support mental health. I make an argument that YouTubers implement new forms of parasocial interactions and affect mental well-being by supplementing social provisions, advocating mental struggles, and offering immersive social experiences. The talk will introduce the methods I used in video and comment analysis. I will discuss the opportunities and challenges of studying online videos in future HCI research.
Dr. Shuo Niu is an assistant professor in Computer Science at Clark University. He holds a Ph.D. in computer science from Virginia Tech. Niu’s research area is human-computer interaction and social computing. His recent work examines social interactions and community activities on video-sharing social media, such as YouTube and TikTok. Particularly, Niu is interested in understanding parasocial interaction between creators and viewers and its implications for mental health, social issue information consumption, and video-sharing technologies. Through analyzing recent and trending YouTube videos, Niu explains how YouTubers engage viewers and what are the critical factors in video viewership. His research incorporates crowdsourcing, natural language processing, and machine learning to analyze extensive video data. Niu published at top-tier human-computer interaction conferences such as ACM CHI, CSCW, and GROUP.