From DBpedia and WordNet hierarchies to LinkedIn and Twitter

Abstract

Previous research has demonstrated the benefits of using linguistic resources to analyze a user's social media profiles in order to learn information about that user. However, numerous linguistic resources exist, raising the question of choosing the appropriate resource. This paper compares Extended WordNet Domains with DBpedia. The comparison takes the form of an investigation of the relationship between users' descriptions of their knowledge and background on LinkedIn with their description of the same characteristics on Twitter. The analysis applied in this study consists of four parts. First, information a user has shared on each service is mined for keywords. These keywords are then linked with terms in DBpedia/Extended WordNet Domains. These terms are ranked in order to generate separate representations of the user's interests and knowledge for LinkedIn and Twitter. Finally, the relationship between these separate representations is examined. In a user study with eight participants, the performance of this analysis using DBpedia is compared with the performance of this analysis using Extended WordNet Domains. The best results were obtained when DBpedia was used.