Characterizing user behavior in online social networks

Internet Measurement Conference, pp. 49-62, 2009.

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Keywords:
social networksocial network topologysocial graphsocial network aggregator websitecharacterizing user behaviorMore(10+)
Weibo:
The data were collected from a social network aggregator website, which after a single authentication enables users to connect to multiple social networks: Orkut, MySpace, Hi5, and LinkedIn

Abstract:

Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first of a kind analysis of user workloads in online social networks. Our study is based on d...More

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Introduction
  • Online social networks (OSNs) have become extremely popular.
  • More than two-thirds of the global online population visit and participate in social networks and blogs.
  • Social networking and blogging account for nearly 10% of all time spent on the Internet.
  • These statistics suggest that OSNs have become a fundamental part of the global online experience.
  • Numerous sites provide social links, for example, networks of professionals and contacts (e.g., LinkedIn, Facebook, MySpace) and networks for sharing content (e.g., Flickr, YouTube)
Highlights
  • Online social networks (OSNs) have become extremely popular
  • In this paper we present a first of a kind analysis of Online social networks workloads based on a clickstream dataset collected from a social network aggregator
  • In this paper we presented a thorough characterization of social network workloads, based on detailed clickstream data summarizing HTTP sessions over a 12-day period of 37,024 users
  • The data were collected from a social network aggregator website, which after a single authentication enables users to connect to multiple social networks: Orkut, MySpace, Hi5, and LinkedIn
  • We presented the clickstream model to characterize user behavior in online social networks
  • Our study uncovered a number of interesting findings, some of which are related to the specific nature of social networking environments
Results
  • The authors' observation highlighted that users actively visiting immediate and non-immediate friends’ pages serves as an empirical precondition for word-of-mouth-based information propagation.
  • When it comes to rich media content like videos and photos, more than 80% of content was found through a 1-hop friend (Figure 7)
Conclusion
  • The authors' measurement analysis provides many interesting findings that the authors think will be useful in various ways.
  • The authors underscored the presence of “silent” user actions, such as browsing a profile page or viewing a photo of a friend.
  • These results led them to classify social interactions into two groups, composed of publicly visible activities and silent activities, respectively
Summary
  • Introduction:

    Online social networks (OSNs) have become extremely popular.
  • More than two-thirds of the global online population visit and participate in social networks and blogs.
  • Social networking and blogging account for nearly 10% of all time spent on the Internet.
  • These statistics suggest that OSNs have become a fundamental part of the global online experience.
  • Numerous sites provide social links, for example, networks of professionals and contacts (e.g., LinkedIn, Facebook, MySpace) and networks for sharing content (e.g., Flickr, YouTube)
  • Results:

    The authors' observation highlighted that users actively visiting immediate and non-immediate friends’ pages serves as an empirical precondition for word-of-mouth-based information propagation.
  • When it comes to rich media content like videos and photos, more than 80% of content was found through a 1-hop friend (Figure 7)
  • Conclusion:

    The authors' measurement analysis provides many interesting findings that the authors think will be useful in various ways.
  • The authors underscored the presence of “silent” user actions, such as browsing a profile page or viewing a photo of a friend.
  • These results led them to classify social interactions into two groups, composed of publicly visible activities and silent activities, respectively
Tables
  • Table1: Summary of the clickstream data
  • Table2: Enumeration of all activities in Orkut and their occurrences in the clickstream data. Events related to browsing are marked with a (*) sign
  • Table3: Comparison of popular user activities across four OSN sites
  • Table4: How users arrive at other people’s pages: preceding locations and activities for every first visit to an immediate and non-immediate friend’s page
Download tables as Excel
Related work
  • There are a rich set of studies on analyzing the workloads of Web 2.0 services. Mislove et al [21] studied graph theoretic properties of OSNs, based on the friends network of Orkut, Flickr, LiveJournal, and YouTube. They confirmed the power-law, small-world, and scale-free properties of these OSN services. Ahn et al [1] studied the network properties of Cyworld, a popular OSN in South Korea. They compared the explicit friend relationship network with the implicit network created by messages exchanged on Cyworld’s guestbook. They found similarities in both networks: the in-degree and out-degree were close to each other and social interaction through the guestbook was highly reciprocal.
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