Understanding Mass Interactions in Online Sports Viewing: Chatting Motives and Usage Patterns

Author(s): Minsam Ko, Seungwoo Choi, Joonwon Lee, Uichin Lee, Aviv Segev
Venue: ACM Transactions on Computer-Human Interaction
Date: February 2016

Type of Experiement: Survey/Multi-Case Study
Sample Size: 1123
Class/Experience Level: Graduate Student
Participant Selection: responded to survey after invited
Data Collection Method: Survey


The authors’ purpose in this article is to find a deeper understanding of mass interactions, specifically in online sports viewing, in regards to interactive experiences, usage motives, and the relationships between usage patterns and motives. This study helps offer better design decisions involved in mass interactions.

The study was conducted through an analysis of 6 million chats on Naver Sports in Korea and 1,123 users that provided answers to a survey. In this study, the chats were observed quantitatively and qualitatively. This means that the number of chats sent throughout a sports game was observed and also the topics of those chats were observed. In reference to the survey, the participants were chosen based on their large contributions to chats on Naver Sports. About 2,000 people were invited and 1,123 users responded. The questions were free-text formatted. From the answers given to the survey the people conducting the survey organized the results into like categories.

The experiment was able to find information that can help make better design decisions on mass interactions. One major factor from the experiment is that they found chat navigation confusing and overwhelming. The authors suggested that this can be changed by categorizing chats or filtering chat data. Also, they found that it is important to users that presence awareness of co-viewers is important.

Overall, the key motives for mass interaction in sports viewing were: sharing feelings, entertainment, sharing information, and wanting to feel a part of a group. This study was able to take online sports viewing and break it down in order to understand the users’ motives, which helps find design patterns fitting this type of mass interaction.