Authors:
Marco Buijs
and
Marco Spruit
Affiliation:
Utrecht University, Netherlands
Keyword(s):
Social Score, PageRank, Web Search, Top-K Ranking, Quality Assessment, Data Analytics, Information Extraction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaborative Filtering
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Software Development
;
Symbolic Systems
Abstract:
There are many ways to determine the importance of Webpages, the most successful one being the PageRank
algorithm. In this paper we describe an alternative ranking method that we call the Social Score method. The
Social Score of a Webpage is based on the number of likes, tweets, bookmarks and other sorts of intensified
information from Social Media platforms. By determining the importance of Webpages based on this kind of
information, ranking becomes based on a democratic system instead of a system in which only web authors
influence the ranking of results. Based on an experiment we conclude that the Social Score is a great alternative
to PageRank that could be used as an additional property to take into account in Web Search Engines.