Comparison of online social relations in volume vs interaction

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Comparison of online social relations in volume vs interaction: a case study of cyworld

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Internet Measurement Conference archive
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement table of contents

Vouliagmeni, Greece

SESSION: Online social networks and IPTV table of contents

Pages 57-70

Year of Publication: 2008

ISBN:978-1-60558-334-1

Authors

Hyunwoo Chun	KAIST, Daejeon, South Korea
Haewoon Kwak	KAIST, Daejeon, South Korea
Young-Ho Eom	KAIST, Daejeon, South Korea
Yong-Yeol Ahn	Center for Complex Network Research, Boston, MA, USA
Sue Moon	KAIST, Daejeon, South Korea
Hawoong Jeong	KAIST, Daejeon, South Korea

Sponsors

SIGCOMM: ACM Special Interest Group on Data Communication
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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ABSTRACT

Online social networking services are among the most popular Internet services according to Alexa.com and have become a key feature in many Internet services. Users interact through various features of online social networking services: making friend relationships, sharing their photos, and writing comments. These friend relationships are expected to become a key to many other features in web services, such as recommendation engines, security measures, online search, and personalization issues. However, we have very limited knowledge on how much interaction actually takes place over friend relationships declared online. A friend relationship only marks the beginning of online interaction.

Does the interaction between users follow the declaration of friend relationship? Does a user interact evenly or lopsidedly with friends? We venture to answer these questions in this work. We construct a network from comments written in guestbooks. A node represents a user and a directed edge a comments from a user to another. We call this network an activity network. Previous work on activity networks include phone-call networks [34, 35] and MSN messenger networks [27]. To our best knowledge, this is the first attempt to compare the explicit friend relationship network and implicit activity network.

We have analyzed structural characteristics of the activity network and compared them with the friends network. Though the activity network is weighted and directed, its structure is similar to the friend relationship network. We report that the in-degree and out-degree distributions are close to each other and the social interaction through the guestbook is highly reciprocated. When we consider only those links in the activity network that are reciprocated, the degree correlation distribution exhibits much more pronounced assortativity than the friends network and places it close to known social networks. The k-core analysis gives yet another corroborating evidence that the friends network deviates from the known social network and has an unusually large number of highly connected cores.

We have delved into the weighted and directed nature of the activity network, and investigated the reciprocity, disparity, and network motifs. We also have observed that peer pressure to stay active online stops building up beyond a certain number of friends.

The activity network has shown topological characteristics similar to the friends network, but thanks to its directed and weighted nature, it has allowed us more in-depth analysis of user interaction.

REFERENCES

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