Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
4-1-2021 12:00 AM
End Date
9-1-2021 12:00 AM
Description
Mutual trust among social network users encourages positive communications; it is critical to study trust in the context of online social networks. We built a trust model and crawled data according to a closeness index. Data was collected from Qzone, an SN service (also known as QQ) in China. We identified 150 QQ users and 3 friends from each of the users; data of these users were collected by Python program. The relationship between trust and closeness was constructed using an ordinary least squares regression model, and the factors that influence trust between social network users were constructed using an endogenous switching regression model. We found that user trust and closeness are positively correlated. A user’s trust is positively related to three closeness indicators: comments, @s to QQ friends (a reminder nudge for attention), and messages. Increasing closeness in social networks has a positive effect on trust formation.
Trust and Closeness: A Mixed Method for Understanding the Relationship of Social Network Users
Online
Mutual trust among social network users encourages positive communications; it is critical to study trust in the context of online social networks. We built a trust model and crawled data according to a closeness index. Data was collected from Qzone, an SN service (also known as QQ) in China. We identified 150 QQ users and 3 friends from each of the users; data of these users were collected by Python program. The relationship between trust and closeness was constructed using an ordinary least squares regression model, and the factors that influence trust between social network users were constructed using an endogenous switching regression model. We found that user trust and closeness are positively correlated. A user’s trust is positively related to three closeness indicators: comments, @s to QQ friends (a reminder nudge for attention), and messages. Increasing closeness in social networks has a positive effect on trust formation.
https://aisel.aisnet.org/hicss-54/dsm/dsm_and_communities/7