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Analysis of MySpace user profiles

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  • Luisa Massari

    (University of Pavia)

Abstract

Online social networks have attracted millions of users, who have integrated social network web sites into their daily life. Users participate to the changes and to the evolution of these sites because they are producers and reviewers of contents that help them to maintain the existing social relationships, make new friends, collaborate and enrich experiences. This paper presents a study of the characteristics of the users of MySpace web site, with the objective of studying relationships and interactions among users and deriving hints about their behavior. The analysis relies on data collected by monitoring the web site for 12 weeks. Typical user behaviors have been derived and classes of users characterized by different levels of participation to the social network have been identified. In particular, the analysis reveals that most of the users actively participate to the social network and specify many personal details. Social networks web sites allow access to such details; the sharing of information about users and their relationships can lead to non-ethic online activities, which threat the privacy and the security of users themselves.

Suggested Citation

  • Luisa Massari, 2010. "Analysis of MySpace user profiles," Information Systems Frontiers, Springer, vol. 12(4), pages 361-367, September.
    • Handle: RePEc:spr:infosf:v:12:y:2010:i:4:d:10.1007_s10796-009-9206-8
    DOI: 10.1007/s10796-009-9206-8
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    References listed on IDEAS

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    1. Cardon, Dominique & Cardon, Christophe, 2007. "The Strength of Weak Cooperation:an Attempt to Understand the Meaning of Web 2.0," MPRA Paper 4581, University Library of Munich, Germany.
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    Cited by:

    1. Chen Wei & Richard Khoury & Simon Fong, 2013. "Web 2.0 Recommendation service by multi-collaborative filtering trust network algorithm," Information Systems Frontiers, Springer, vol. 15(4), pages 533-551, September.
    2. Harleen Kaur & Shafqat Ul Ahsaan & Bhavya Alankar & Victor Chang, 2021. "A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets," Information Systems Frontiers, Springer, vol. 23(6), pages 1417-1429, December.
    3. Kawaljeet Kaur Kapoor & Kuttimani Tamilmani & Nripendra P. Rana & Pushp Patil & Yogesh K. Dwivedi & Sridhar Nerur, 2018. "Advances in Social Media Research: Past, Present and Future," Information Systems Frontiers, Springer, vol. 20(3), pages 531-558, June.
    4. Chulhwan Chris Bang, 2015. "Information systems frontiers: Keyword analysis and classification," Information Systems Frontiers, Springer, vol. 17(1), pages 217-237, February.
    5. Gohar Feroz Khan, 2013. "Social media-based systems: an emerging area of information systems research and practice," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 159-180, April.

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