ELM-based analysis of user bullet screen behavior in Bilibili online teaching videos

Exploring the influencing factors of user bullet screen commenting behavior in online teaching videos can provide references for optimizing related functions of bullet screen video websites and promoting user interaction behavior, thus to improve the user experience of online learning. Based on the Elaboration Likelihood Model (ELM) and the use of the hierarchical regression analysis method, the influencing factors of the dual path of user bullet screen commenting behavior in online teaching videos were summarized, and 980 English teaching video data were collected from Bilibili, and hypotheses were proposed and validated using the data. The results of the hierarchical regression analysis showed that under the central path, the video's propagation effect had a significantly positive impact on user bullet screen commenting behavior; under the peripheral path, the attractiveness of the information source and the author's contribution to the video had a significantly positive and negative effect on bullet screen commenting behavior, respectively. Relevant suggestions were accordingly proposed for bullet screen video website operators to understand user interaction behavior and improve their services.