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Proposal of a New Social Signal for Excluding Common Web Pages in Multiple Social Networking Services

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9795))

Abstract

In recent years, a social signal that is given by a social network service (SNS) on the World Wide Web where a huge quantity of information exists has attracted attention. A social signal is an index that measures how much a web page is a hot topic among users on the SNS. For instance, the number of “retweets” on Twitter and the number of “likes” on Facebook are social signals. Generally speaking, a social signal is evaluated by the administrator of the SNS and displayed on a web page and can be read by anyone. By utilizing a social signal, acquiring current hot-topic web pages efficiently is expected. However, if web pages are simply chosen on the basis of the magnitude of the social signal of an SNS that has many users, most of web pages will be the common web pages among SNSs, and the characteristic web pages that can only be seen when using a certain SNS is buried in the common web pages. Therefore, in this paper, we propose a new social signal that assesses the degree to which a certain web page is a hot-topic web only in an SNS by combining the social signals of SNSs. As a result of a performance evaluation, we show that by acquiring web pages on the basis of the magnitude of the proposed new social signal, hot-topic web pages in multiple SNSs are excludable.

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References

  1. Bing. https://www.bing.com/

  2. Facebook. https://www.facebook.com/

  3. Google. https://www.google.com/

  4. Google+. https://plus.google.com/

  5. LinkedIn. https://www.linkedin.com/

  6. Pinterest. www.pinterest.com

  7. Pocket. https://getpocket.com/

  8. Twitter. https://twitter.com/

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Acknowledgment

This work was partly supported by Dayz Inc.

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Correspondence to Hiroyuki Hisamatsu .

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© 2016 Springer International Publishing Switzerland

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Hisamatsu, H., Tsugawa, T. (2016). Proposal of a New Social Signal for Excluding Common Web Pages in Multiple Social Networking Services. In: Nguyen, H., Snasel, V. (eds) Computational Social Networks. CSoNet 2016. Lecture Notes in Computer Science(), vol 9795. Springer, Cham. https://doi.org/10.1007/978-3-319-42345-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-42345-6_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42344-9

  • Online ISBN: 978-3-319-42345-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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