Content mining of microblogs

Emergence of Web 2.0, internet users can share their contents with other users using social networks. Microblogs are an sub area of social networks. Twitter is a popular application that people share their contents which are called as tweet with each other. Users generally follow microbloggers according to their field of interest. Followers expect from other users who are followed by followers to share their contents which reflect follower?s field of interest.In this work unprocessed data on microblogs are evaluated with respect to how they reflect their categories. Migrobloggers? category information, which is one of the four categories that are economy sport, entertainment or technology, is taken from wefollow.com application. 3337 RSS news feeds, whose category labels are same with microbloggers? contributions, are used as training data for classification. Unlike the similar studies if a feature of microblog doesn?t appear in RSS news feeds as a feature, this feature is omitted so abbreviations and nonsense words in microblogs can be eliminated.In this study two types of users? contributions are taken as test data. These users are normal microbloggers and bots. Classification results show that bots provide more categorical content than normal users. However, normal users and bot users whose categorical information is sport classification of content performance are better than other users who provide content about other three categories. The worst classification performance is obtained from users whose categorical information is technology.Our proposed modal is also compared the modal which uses only tweets, which are also used in proposed modal, in training and testing phases. Proposed modal gives better classification performance than the modal which uses only tweets in training and testing phases.