Abstract
Online social networks serves as an arena for its members to get in touch with each other, mutually share their information, ideas among themselves. In online social networks the members usually proclaim a profile, which consists of work and education, arts and entertainment and some basic information like gender, e-mail, etc., Such profile facilitates in spotting people, know about their interest, and interact with them in need. The intention of this research is to devise an algorithm to extract information such as name, email address, gender and interest of facebook users from a URL and to predict the gender if unspecified. The Dataset used in this work is a list of 100 million Facebook URLs. This research work paves a way to identify the email communities in Facebook. The outcome of this research reveals the fact that most of the email domains of the facebook user’s fall into yahoo, hotmail, Gmail and msn. The other domains are with least number of users. The users with Yahoo id are higher when compared to other email domains. It also discloses that majority of the interest of facebook members is towards sports. It is followed by music, technology, travelling, God and Temple run, PC gaming.
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Nancy, P., Geetha Ramani, R. (2014). Information Harvest from Social Network Data (Facebook 100 million URLS). In: Kim, H., Ao, SI., Amouzegar, M., Rieger, B. (eds) IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol 247. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6818-5_36
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DOI: https://doi.org/10.1007/978-94-007-6818-5_36
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