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Leveraging Microblogs for Resource Ranking

  • Conference paper
SOFSEM 2012: Theory and Practice of Computer Science (SOFSEM 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7147))

Abstract

In order to compute page rankings, search algorithms primarily utilize information related to page content and link structure. Microblog as a phenomenon of today provides additional, potentially relevant, information – short messages often containing hypertext links to web resources. Such source is particularly valuable when considering a temporal aspect of information, which is being published every second. In this paper we present a method for resource ranking based on Twitter data structure processing. We apply various graph algorithms leveraging the notion of a node centrality in order to deduce microblog-based resource ranking. Our method ranks a microblog user based on his followers count with respect to a number of (re)posts and reflects it into resource ranking. The evaluation of the method showed that micro-based resource ranking a) can not be substituted by a common form of an explicit user rating, and b) has the great potential for search improvement.

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Authors and Affiliations

  1. Faculty of Informatics and Information Technologies, Slovak University of Technology, Ilkovičova 3, 842 16, Bratislava 4, Slovakia

    Tomáš Majer & Marián Šimko

Authors
  1. Tomáš Majer
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  2. Marián Šimko
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Editor information

Editors and Affiliations

  1. Faculty of Informatics and Information Technologies, Institute of Informatics and Software Engineering, Slovak University of Technology in Bratislava, Ilkovičova 3, 842 16, Bratislava 4, Slovakia

    Mária Bieliková

  2. Dept. of Intelligent Systems and Business Informatics, Alpen-Adria-Universität Klagenfurt, Universitätsstr. 65-57, 9020, Klagenfurt, Austria

    Gerhard Friedrich

  3. Department of Computer Science, University of Oxford, UK

    Georg Gottlob

  4. Security Engineering Group, Technische Universität Darmstadt, Hochschulstr. 10, 64289, Darmstadt, Germany

    Stefan Katzenbeisser

  5. University of Illinois at Chicago, Dept. of Math., Stat. and Comp. Sci, 851 S. Morgan Street, Chicago, IL 60607-7045, USA; and University of Szeged, Research Group on Artificial Intelligence of the Hungarian Academy of Sciences, 6701 Szeged, Postafiók, Hungary

    György Turán

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© 2012 Springer-Verlag Berlin Heidelberg

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Majer, T., Šimko, M. (2012). Leveraging Microblogs for Resource Ranking. In: Bieliková, M., Friedrich, G., Gottlob, G., Katzenbeisser, S., Turán, G. (eds) SOFSEM 2012: Theory and Practice of Computer Science. SOFSEM 2012. Lecture Notes in Computer Science, vol 7147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27660-6_42

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  • DOI: https://doi.org/10.1007/978-3-642-27660-6_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27659-0

  • Online ISBN: 978-3-642-27660-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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