抄録
In social networking service (SNS), popularity of an entity (e.g., person, company and place) roles an important criterion for people and organizations, and several studies pose to predict the popularity. Although recent papers which addressing the problem of predicting popularity use the attributes of entity itself, typically, the popularity of entities depends on the attributes of other semantically related entities. Hence, we take an approach exploiting the background semantic structure of the entities. Usually, many factors affect a person's popularity: the occupation, the parents, the birthplace, etc. All affect popularity. Predicting the popularity with the semantic structure is almost equivalent to solving the question: What type of relation most affects user preferences for an entity on a social medium? Our proposed method for popularity prediction is presented herein for predicting popularity, on a social medium of a given entity as a function of information of semantically related entities using DBpedia as a data source. DBpedia is a large semantic network produced by the semantic web community. The method has two techniques: (1) integrating accounts on SNS and DBpedia and (2) feature generation based on relations among entities. This is the first paper to propose an analysis method for SNS using semantic network.
