Characterizing user behavior in online social networks

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 detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we crawled the social network topology of Orkut, so...

• Type: Conference
• Year: 2009
• Where: IMC
• Authors: Fabrício Benevenuto, Tiago Rodrigues, Meeyoung Cha, Virgílio A. F. Almeida
• Keywords: Clickstream Data, IMC 2009, Internet Technology, Social Network, Social Network Workloads