Audience size, moderator activity, gender, and content diversity: Exploring user participation and financial commitment on Twitch.tv

2022 ◽  
pp. 146144482110699
Author(s):  
Grace H Wolff ◽  
Cuihua Shen
Keyword(s):  
Social Influence ◽  
Online Communities ◽  
Financial Support ◽  
User Participation ◽  
Behavioral Data ◽  
Live Streaming ◽  
Collective Effort ◽  
Content Diversity ◽  
Streaming Service ◽  
Individual Participation

User participation has long been recognized as a cornerstone of thriving online communities. Social live-streaming service (SLSS) communities are built on a subscription-based model and rely on viewers’ participation and financial support. Using the collective effort model and heuristics of social influence, this study examines the influence of streamer and viewer behaviors on viewers’ participation and financial commitment on the SLSS, Twitch.tv. Findings from behavioral data collected over 7 weeks show larger audiences diminish individual participation and financial commitment while moderation may encourage more. Female streamers benefit from increased moderation, earning two to three times more in financial commitment compared to men, who streamed more frequently and for longer durations but attracted much smaller audiences. Viewers’ participation and financial commitment did not differ across streams with more content diversity. Our results demonstrate how group factors influence individual participation and financial commitment in newer subscription-based media.

scholarly journals Factors Affecting the Popularity of Video Content on Live-Streaming Services: Focusing on V Live, the South Korean Live-Streaming Service

2020 ◽  
Vol 12 (5) ◽  
pp. 1784 ◽  
Author(s):  
Minjeong Ham ◽  
Sang Woo Lee
Keyword(s):  
Live Streaming ◽  
Cumulative Number ◽  
Video Content ◽  
Factors Affecting ◽  
South Korean ◽  
Key Characteristics ◽  
Quality Video ◽  
Streaming Service ◽  
High Quality Video ◽  
Production Company

Naver V Live, a South Korean live-streaming service, showcases video contents specific to the entertainment industry, such as K-pop and music. On V Live, K-pop stars and their fans can interact directly in a natural way, and V Live provides high-quality video content with novel topics. This study has identified key characteristics of video content that affect its popularity. A total of 620 video contents of five leading Star channels were classified on the basis of production company, type of video content, and whether it was live-streamed or not. The popularity of video content was measured by the number of comments, hearts, and views. To control potential bias, additional variables were set as control variables—such as the number of channel subscribers, mini-album sales, if the video content was previewed, and cumulative number of days since the video content was uploaded. For analysis, a hierarchical linear regression was conducted. The findings suggest future directions in video content planning.

scholarly journals Scalable Live Streaming Service Based on Interoverlay Optimization

2007 ◽  
Vol 18 (12) ◽  
pp. 1663-1674 ◽  
Author(s):  
Xiaofei Liao ◽  
Hai Jin ◽  
Yunhao Liu ◽  
Lionel M. Ni
Keyword(s):  
Live Streaming ◽  
Streaming Service

scholarly journals HetInf: Social Influence Prediction With Heterogeneous Graph Neural Network

2022 ◽  
Vol 9 ◽  
Author(s):  
Liqun Gao ◽  
Haiyang Wang ◽  
Zhouran Zhang ◽  
Hongwu Zhuang ◽  
Bin Zhou
Keyword(s):  
Neural Network ◽  
Social Network ◽  
Social Influence ◽  
User Behavior ◽  
Online Advertising ◽  
Live Streaming ◽  
Network Applications ◽  
Key Factor ◽  
Study Designs ◽  
The Impact

With the continuous enrichment of social network applications, such as TikTok, Weibo, Twitter, and others, social media have become an indispensable part of our lives. Web users can participate in their favorite events or pay attention to people they like. The “heterogeneous” influence between events and users can be effectively modeled, and users’ potential future behaviors can be predicted, so as to facilitate applications such as recommendations and online advertising. For example, a user’s favorite live streaming host (user) recommends certain products (event), can we predict whether the user will buy these products in the future? The majority of studies are based on a homogeneous graph neural network to model the influence between users. However, these studies ignore the impact of events on users in reality. For instance, when users purchase commodities through live streaming channels, in addition to the factors of the host, the commodity is also a key factor that influences the behavior of users. This study designs an influence prediction model based on a heterogeneous neural network HetInf. Specifically, we first constructed the heterogeneous social influence network according to the relationship between event nodes and user nodes, then sampled the user heterogeneous subgraph for each user, extracted the relevant node features, and finally predicted the probability of user behavior through the heterogeneous neural network model. We conducted comprehensive experiments on two large social network datasets. Furthermore, the experimental results show that HetInf is significantly superior to the previous homogeneous neural network methods.

iGridMedia: The system to provide low delay peer-to-peer live streaming service over internet

2009 ◽  
Vol 3 (3) ◽  
pp. 175-185 ◽  
Author(s):  
Meng Zhang ◽  
Lifeng Sun ◽  
Yechang Fang ◽  
Shiqiang Yang
Keyword(s):  
Peer To Peer ◽  
Live Streaming ◽  
Low Delay ◽  
Streaming Service
Sign in / Sign up

Export Citation Format

Share Document