Understanding User Immersion in Online Short Video Interaction

Short video (SV) online streaming has been one of the most popular Internet applications in recent years. When browsing SVs, users gradually immerse themselves and derive relaxation or knowledge. Whereas prolonged browsing will lead to a decline in positive feelings, users continue due to inertia, resulting in decreased satisfaction. Immersion is shown to be an essential factor for users' positive experience and highly related to users' interactions in film, games, and virtual reality. However, immersion in SV interaction is still unexplored, which differs from the previously studied scenarios essentially because SV delivery is fragmented, discrete, and with limited time for each video. In this paper, we aim to make an extensive understanding of user immersion in online short video interaction, include related factors, detecting possibility, and satisfaction representation. We conduct a three-step user study on real SV browsing, including an online survey, a field study, and a lab study with EEG signals. The user study reveals that immersion is a common feeling in SV interaction, and it is related to video features, personalization of recommendations, user mood, and interaction behaviors. Specifically, prolonged browsing leads to a significant decrease in immersion. Furthermore, analyses of EEG signals demonstrate that the prefrontal lobe and parietal lobe of the gamma band are associated with immersion. Besides, immersion prediction experiments achieve encouraging results, showing that user immersion status is predictable and EEG signals do help improve prediction performance. Moreover, correlation analysis indicates that the predicted immersion is more representative of user satisfaction than user behaviors, revealing the potential of immersion as an indicator of satisfaction in the recommender system. To the best of our knowledge, it is the first study on user immersion in real online SV interaction scenarios, and our findings are enlightening for SV users and recommender system designers.