Studying and understanding characteristics of post-syncing practice and goal in social network sites

Zhang, P., Liu, B., Ding, X. , Lu, T., Gu, H. and Gu, N. (2021) Studying and understanding characteristics of post-syncing practice and goal in social network sites. ACM Transactions on the Web, 15(4), 16. (doi: 10.1145/3457986)

Abstract

Many popular social network sites (SNSs) provide the post-syncing functionality, which allows users to synchronize posts automatically among different SNSs. Nowadays there exists divergence on this functionality from the view of sink SNS. The key to solving this problem is to understand the characteristics of users’ post-syncing practice and goals and evaluate whether they are consistent with an SNS’s norms, cultures, and goals. However, studying and understanding the characteristics of post-syncing practice and goal are challenging tasks as a result of the difficulty of data sampling and the complexity of post-syncing behavior. In this article, we focus on investigating this question by quantitative analysis in combination with qualitative analysis. In the quantitative study, by utilizing 211,233 synced-posts sampled from Weibo, we aim to investigate characteristics of post-syncing from three perspectives: user, content, and goal. The results suggest that post-syncing plays an important role in exhibiting one’s current activities, creations, and skills as well as advertisements but involves a risk of exhibiting personal sensitive profiles. To understand the results, we present an interview-based qualitative study based on thematic analysis. It indicates that the publicity, urgency, and remarkableness of contents and differences of social affordances and social circles between sink SNS and source SNS as well as the one-time consent of post-syncing authentication jointly account for the major role of post-syncing. Based on these results, we propose insights for post-syncing functionality’s adoption, design, and promotion.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ding, Dr Sharon
• Authors: Zhang, P., Liu, B., Ding, X., Lu, T., Gu, H., and Gu, N.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: ACM Transactions on the Web
• Publisher: Association for Computing Machinery (ACM)
• ISSN: 1559-1131
• ISSN (Online): 1559-114X
• Published Online: 14 June 2021