May Shalaby and Ahmed Rafea
User influence, Twitter, Intelligent Data Analysis, Pattern Recognition
The content in online communities has become strategically important that the identification of the influential members can benefit all in developing business opportunities, forging political agendas, discussing social and societal issues, and can lead to many interesting innovative applications. Among social networking websites, one of the most important is Twitter. We are interested in identifying the topic-specific influential members in Twitter. Influence of a user may be determined by many factors. The objective of the study is to identify the factors that play a crucial role in the measurement of a Twitter user’s influence, examine how the different factors impact the influence ranking and how these factors can be expressed in a mathematical model. We devise a model to score the strength of a user’s influence using basic user features collected for a group of users discussing a common issue. We then validate our results using Klout as a reference point, and then further verify our ranking of the users by manual evaluation.
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