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.
['Tomáš Majer', 'Marián Šimko']
Leveraging microblogs for resource ranking