TY - JOUR
T1 - Understanding how retweets influence the behaviors of social networking service users via agent-based simulation
AU - Yan, Yizhou
AU - Toriumi, Fujio
AU - Sugawara, Toshiharu
N1 - Funding Information:
This work is partly supported by JSPS KAKENHI Grant Numbers 20H04245, 19H02376, and 18H03498.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - The retweet is a characteristic mechanism of several social network services/social media, such as Facebook, Twitter, and Weibo. By retweeting tweet, users can share an article with their friends and followers. However, it is not clear how retweets affect the dominant behaviors of users. Therefore, this study investigates the impact of retweets on the behavior of social media users from the perspective of networked game theory, and how the existence of the retweet mechanism in social media promotes or reduces the willingness of users to post and comment on articles. To address these issues, we propose the retweet reward game model and quote tweet reward game model by adding the retweet and quote tweet mechanisms to a relatively simple social networking service model known as the reward game. Subsequently, we conduct simulation-based experiments to understand the influence of retweets on the user behavior on various networks. It is demonstrated that users will be more willing to post new articles with a retweet mechanism, and quote retweets are more beneficial to users, as users can expect to spread their information and their own comments on already posted articles.
AB - The retweet is a characteristic mechanism of several social network services/social media, such as Facebook, Twitter, and Weibo. By retweeting tweet, users can share an article with their friends and followers. However, it is not clear how retweets affect the dominant behaviors of users. Therefore, this study investigates the impact of retweets on the behavior of social media users from the perspective of networked game theory, and how the existence of the retweet mechanism in social media promotes or reduces the willingness of users to post and comment on articles. To address these issues, we propose the retweet reward game model and quote tweet reward game model by adding the retweet and quote tweet mechanisms to a relatively simple social networking service model known as the reward game. Subsequently, we conduct simulation-based experiments to understand the influence of retweets on the user behavior on various networks. It is demonstrated that users will be more willing to post new articles with a retweet mechanism, and quote retweets are more beneficial to users, as users can expect to spread their information and their own comments on already posted articles.
KW - Agent-based simulation
KW - Complex network
KW - Facebook
KW - Meta-norms game
KW - Quote tweet
KW - Retweet
KW - Social media
KW - Twitter
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U2 - 10.1186/s40649-021-00099-8
DO - 10.1186/s40649-021-00099-8
M3 - Article
AN - SCOPUS:85114784508
SN - 2197-4314
VL - 8
JO - Computational Social Networks
JF - Computational Social Networks
IS - 1
M1 - 18
ER -