TY - GEN
T1 - More Than a Button
T2 - 2025 2nd International Conference on Artificial Intelligence, Digital Media Technology and Interaction Design, ICADI 2025
AU - Wang, Weizhao
AU - Liu, Hongde
AU - Wang, Jun
AU - Huang, Yuanxin
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2026/4/19
Y1 - 2026/4/19
N2 - As user traffic becomes a key source of competitive advantage on UGC video platforms, creators have begun actively initiating interactions in their videos to boost users' positive engagement and ultimately promote content monetization. Based on dual-processing theory, this study investigates how creator-initiated affective (low-cognitive-effort) and cognitive (high-cognitive-effort) interactions influence users' online tipping. Specifically, we employed large-scale data collection via the Bilibili API using Python-based crawling scripts, followed by computational preprocessing to construct a dataset of 353,501 videos from 25,277 creators. We applied regression algorithms and interaction effect estimation to empirically test our hypotheses. Furthermore, content type plays an important moderating role. This study not only enriches theoretical understanding of interactive media effects but also demonstrates how computational approaches - such as automated data mining, algorithmic modeling, and large-scale empirical analysis - can advance the application of computer technology in digital media interaction research.
AB - As user traffic becomes a key source of competitive advantage on UGC video platforms, creators have begun actively initiating interactions in their videos to boost users' positive engagement and ultimately promote content monetization. Based on dual-processing theory, this study investigates how creator-initiated affective (low-cognitive-effort) and cognitive (high-cognitive-effort) interactions influence users' online tipping. Specifically, we employed large-scale data collection via the Bilibili API using Python-based crawling scripts, followed by computational preprocessing to construct a dataset of 353,501 videos from 25,277 creators. We applied regression algorithms and interaction effect estimation to empirically test our hypotheses. Furthermore, content type plays an important moderating role. This study not only enriches theoretical understanding of interactive media effects but also demonstrates how computational approaches - such as automated data mining, algorithmic modeling, and large-scale empirical analysis - can advance the application of computer technology in digital media interaction research.
KW - Content type
KW - Creator-initiated interaction
KW - Online tipping
KW - UGC Video Platforms
UR - https://www.scopus.com/pages/publications/105038613302
U2 - 10.1145/3795926.3795960
DO - 10.1145/3795926.3795960
M3 - 会议稿件
AN - SCOPUS:105038613302
T3 - Proceedings of 2025 2nd International Conference on Artificial Intelligence, Digital Media Technology and Interaction Design, ICADI 2025
SP - 206
EP - 210
BT - Proceedings of 2025 2nd International Conference on Artificial Intelligence, Digital Media Technology and Interaction Design, ICADI 2025
PB - Association for Computing Machinery, Inc
Y2 - 28 November 2025 through 30 November 2025
ER -