Influential user detection on Twitter: Analyzing effect of focus rate

Social media usage has increased marginally in the last decade and it is still continuing to grow. Companies, data scientists, and researchers are trying to infer meaningful information from this vast amount of data. One of the most important target applications is to find influential people in these networks. This information can serve many purposes such as; user or content recommendation, viral marketing, and user modeling. Social media is divided into subcategories like where one can share photos (i.e. Instagram, Flickr), video or music (i.e. Youtube, Last.fm), restaurant suggestions like Foursquare, or text like Twitter. Twitter is more of an idea and news sharing media than other types of social media and it has a huge amount of public profiles. These features of Twitter make it a more interesting and valuable media to research on. In this paper, we are addressing to identify topical authorities/influential users in Twitter. We provide a novel representation of users' topical interests called focus rate. We incorporate nodal features into network features and introduce a modified version of Pagerank algorithm which efficiently analyzes topical influence of users. Experimental results show that focus rate of users on specific topics increase their influence scores and lead to higher information diffusion. We use also distributed computing environment which enables to work with large data sets. We demonstrate our results on Turkish Twitter messages. For the best of our knowledge, this is the first influence analysis on Twitter that is conducted for Turkish language.