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The power of comments： fostering social interactions in microblog networks

Frontiers of Computer Science 2016年第5期10卷 889-907页

作者： Tianyi WANG Yang CHEN Yi WANG Bolun WANG Gang WANG Xing LI Haitao ZHENG Ben Y. ZHAO Department of Electronic Engineering Tsinghua University Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology Beijing 100084 China Department of Computer Science University of California Santa Barbara CA 93106-5110 USA School of Computer Science Fudan University Shanghai 201203 China Department of Mathematics Syracuse University Syracuse NY 13244-1150 USA

Today＇s ubiquitous online social networks serve multiple purposes, including social communication （Face- book, Renren）, and news dissemination （Twitter）. But how does a social network＇s design define its functionality？ An- swering this would need social network providers to take a proactive role in defining and guiding user behavior. In this paper, we first take a step to answer this question with a data-driven approach, through measurement and anal- ysis of the Sina Weibo microblogging service. Often com- pared to Twitter because of its format, Weibo is interesting for our analysis because it serves as a social communication tool and a platform for news dissemination, too. While similar to Twitter in functionality, Weibo provides a distinguishing feature, comments, allowing users to form threaded con- versations around a single tweet. Our study focuses on this feature, and how it contributes to interactions and improves social engagement. We use analysis of comment interactions to uncover their role in social interactivity, and use comment graphs to demonstrate the structure of Weibo users interac- tions. Finally, we present a case study that shows the impact of comments in malicious user detection, a key application on microblogging systems. That is, using properties of com- ments significantly improves the accuracy in both modeling and detection of malicious users.

关键词： microblogs comments social and interactiongraph user behavior

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