T. Majer, and M. Simko. SOFSEM 2012: Theory and Practice of Computer Science, volume 7147 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2012)
DOI: 10.1007/978-3-642-27660-6_42
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.
%0 Book Section
%1 citeulike:14057398
%A Majer, Tomás
%A Simko, Marián
%B SOFSEM 2012: Theory and Practice of Computer Science
%D 2012
%E Bieliková, Mária
%E Friedrich, Gerhard
%E Gottlob, Georg
%E Katzenbeisser, Stefan
%E Turán, György
%I Springer Berlin Heidelberg
%K social-search twitter
%P 518--529
%R 10.1007/978-3-642-27660-6_42
%T Leveraging Microblogs for Resource Ranking
%U http://dx.doi.org/10.1007/978-3-642-27660-6_42
%V 7147
%X 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.
@incollection{citeulike:14057398,
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.}},
added-at = {2018-03-19T12:24:51.000+0100},
author = {Majer, Tom\'{a}\v{s} and \v{S}imko, Mari\'{a}n},
biburl = {https://www.bibsonomy.org/bibtex/2e008e8fde7f70304c70f38a6e00868a4/aho},
booktitle = {SOFSEM 2012: Theory and Practice of Computer Science},
citeulike-article-id = {14057398},
citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-27660-6_42},
citeulike-linkout-1 = {http://link.springer.com/chapter/10.1007/978-3-642-27660-6_42},
doi = {10.1007/978-3-642-27660-6_42},
editor = {Bielikov\'{a}, M\'{a}ria and Friedrich, Gerhard and Gottlob, Georg and Katzenbeisser, Stefan and Tur\'{a}n, Gy\"{o}rgy},
interhash = {e0093c85557cc707ad5e0cd24991cfc5},
intrahash = {e008e8fde7f70304c70f38a6e00868a4},
keywords = {social-search twitter},
pages = {518--529},
posted-at = {2016-06-06 02:33:39},
priority = {2},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Computer Science},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{Leveraging Microblogs for Resource Ranking}},
url = {http://dx.doi.org/10.1007/978-3-642-27660-6_42},
volume = 7147,
year = 2012
}