Abstract
Online communities are filled with comments of loyal readers or first-time viewers, that are constantly creating and sharing information at an unprecedented level, resulting in millions of messages containing opinions, ideas, needs and beliefs of Internet users. Therefore, businesses companies are very interested in finding influential users and encouraging them to create positive influence. Influential users represent users with the ability to influence individual’s attitudes in a desired way with relative frequency. We present an empirical analysis on influential users identification problem in Twitter. Our proposed approach considers that the influential level of users can be detected by considering its communication patterns, by means of particular writing style features as well as behavioral features. Performed experiments on more that 7000 users profiles, indicate that it is possible to automatically identify influential users among the members of a social networking community, and also it obtains competitive results against several state-of-the-art methods.
This work was partially supported by CONACyT México Project Grant CB-2010/153315, and SEP-PROMEP UAM-PTC-380/48510349. We also thank to UAM Unidad Cuajimalpa and SNI-CONACyT for their support.
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Ramírez-de-la-Rosa, G., Villatoro-Tello, E., Jiménez-Salazar, H., Sánchez-Sánchez, C. (2014). Towards Automatic Detection of User Influence in Twitter by Means of Stylistic and Behavioral Features. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Human-Inspired Computing and Its Applications. MICAI 2014. Lecture Notes in Computer Science(), vol 8856. Springer, Cham. https://doi.org/10.1007/978-3-319-13647-9_23
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DOI: https://doi.org/10.1007/978-3-319-13647-9_23
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