“数据•营销•创新”论坛：Social Listening To In-Consumption Moment-to-Moment Dynamics: The Case of Movie Appreciation and Live Comments
讲座题目：Social Listening To In-Consumption Moment-to-Moment Dynamics: The Case of Movie Appreciation and Live Comments
演讲人：Professor Wenbo Wang
Professor Wenbo Wang is an associate Professor with tenure in the Marketing Department at the Hong Kong University of Science and Technology. He earned PhD degree at New York University Stern Business School. Professor Wang’s research interests focus on social media, big data, green Marketing. He published at Marketing Science, Journal of Marketing Research, Journal of Consumer Research. Professor Wang won the Early Career Award from the Hong Kong Government RGC. Professor Wang won Franklin Prize for Teaching Excellence. He has been teaching digital marketing and big data for EMBA, EDP, UG programs. Professor Wang was a research scientist or training consultant with P&G, Boehringer Ingelheim, Citibank, Apple Daily, Hainan Airline, Chongqing Government, etc.
时间：2018年9月13日（周四）上午10:00 - 12:00
Abstract: Consumption of entertainment products, such as movies, video games, and sports events, takes place over a nontrivial time period. During these experiences, consumers are likely to encounter temporal variations in the content of consumption, to which they may react in real time. Compared with existing in-consumption analysis (e.g., eye tracking, neural activity analysis), listening to in-consumption consumers’ voices on social media has great potential. Our paper proposes a new approach for in-consumption social listening and demonstrates its value in the context of online movie watching wherein viewers can react to movie content with live comments. Specifically, we propose to listen to the live comments through a novel measure, moment-to-moment synchronicity (MTMS), to capture consumers’ in-consumption engagement. MTMS refers to the synchronicity between temporal variations in the volume of live comments and those in movie content mined from unstructured video, audio, and text data from movies (i.e., camera motion, shot length, sound loudness, pitch, and spoken lines). We demonstrate that MTMS has a significant impact on viewers’ post-consumption appreciation of movies, and it can be evaluated at finer level to identify engaging content. Finally, we discuss the relation between MTMS and existing in-consumption measures and the value of integrating supply-side content information into in-consumption analysis.