Paper Info
Title
Learning How Spectator Reactions Affect Popularity on Twitch
Abstract
Gameplay live streaming has become a highly demanded online entertainment traffic on services like Twitch.tv. The live chat concurrent with on-air streams allows viewers to talk about the content with each other. This new form of online content carries numerous spectator reactions, including text and emojis, and brings a unique opportunity to predict which video becomes popular in the end. This study presents a prediction model of viewer counts on a newly compiled dataset describing the video popularity of Twitch live streams and chat reactions of spectators. Our analysis demonstrates that the spectator reactions captured from the early-stage of live streams, such as the first 15 minutes out of several hours of content, hold essential markers of the eventual popularity of video streams. We discuss the implications of the findings and future directions.
Year
DOI
Venue
2020
10.1109/BigComp48618.2020.00-84
2020 IEEE International Conference on Big Data and Smart Computing (BigComp)
Keywords
DocType
ISSN
Twitch chat analysis,User response,Live streaming,Video popularity prediction,Big data analytics
Conference
2375-933X
ISBN
Citations 
PageRank 
978-1-7281-6035-1
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Jeong-min Kim1608.97
Kunwoo Park213614.51
Hyeonho Song300.68
Jaimie Y. Park400.34
Meeyoung Cha54391237.20