Marketing Seminar（2018-17）

Topic:In-Consumption Social Listening with Moment-to-Moment Unstructured Data: The Case of Movie Appreciation and Live Comments

Speaker:Qiang Zhang, PhD Candidate in Hong Kong University of Science and Technology

Time:Wednesday, 17 October, 10:00-11:30

Location:Room 216, Guanghua Building 2

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.

Introduction:

Qiang Zhang is a doctoral candidate in marketing at Hong Kong University of Science and Technology. He received his bachelor's degree in management science from Nanjing University. His current research focuses on modeling digital footprints of consumers when they search products and consume content online. His research aims to provide managerial insights for video and live streaming platforms as well as online retailers. In terms of research methods, he has broad interests in analyzing unstructured data, structural econometric models and Bayesian methods.

Your participation is warmly welcomed!