Characterizing user navigation and interactions in online social networks

an article by Fabrício Benevenuto (Federal University of Ouro Preto, Brazil),  Tiago Rodrigues and Virgílio Almeida (Federal University of Minas Gerais, Brazil) and Meeyoung Cha (Graduate School of Culture Technology, KAIST, Republic of Korea) published in Information Sciences Volume 195 (15^th July 2012)

Abstract

Understanding how users navigate and interact 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 an in-depth analysis of user workloads in online social networks.

This 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 gather the social network topology of Orkut, so that we could analyse user interaction data in light of the social graph.

Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends’ and non-immediate friends’ pages.

Results show that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities. Consequently, compared to using only crawled data, silent interactions like browsing friends’ pages increase the measured level of interaction among users.

Additionally, we find that friends requesting content are often within close geographical proximity of the uploader. We also discuss a series of implications of our findings for efficient system and interface design as well as for advertisement placement in online social networks.

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