Abstract
Social networks build up a representation of the social structure on the Internet by enabling new ways of communication and understanding of human relations. These networks generate big amounts of information on which we can apply mining techniques in order to extract knowledge. Different works have studied many aspects of social networks, but just a few of them focused on text mining in social networks. In this work, we focus on the Twitter social network features and specifically on the use of this network by a representative, and well-known, user’s behaviour. We extracted all the contents that previously Senator and then President Barack Obama has shared in this service in the course of the last 3 years and applied a text-analysis knowledge discovery methodology to it. This methodology allowed us to build a meaning-making process on our dataset. In this process, we successfully conducted a cluster analysis that helped collecting Barack Obama’s Twitter contents in groups. Studying the results, we perceived that these clusters could be interpreted as a mirror of his political strategy. Finally, we discuss the application of this method for other social networks.
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Guidi, M., Ruiz-Agundez, I., Canga-Sanchez, I. (2012). Knowledge Mining from the Twitter Social Network: The Case of Barack Obama. In: Abraham, A. (eds) Computational Social Networks. Springer, London. https://doi.org/10.1007/978-1-4471-4054-2_9
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DOI: https://doi.org/10.1007/978-1-4471-4054-2_9
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