Sandra Servia Rodríguez

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The massive growth of GPS equipped smartphones coupled with the increasing importance of Social Media has led to the emergence of new services over LBSNs (Location-based Social Networks) where both, opinions and location, are shared. This proactive attitude allow us to consider citizens as sensors in motion whose information supports our approach:(More)
In information-rich environments, the competition for users' attention leads to a flood of content from which people often find hard to sort out the most relevant and useful pieces. Using Twitter as a case study, we applied an attention economy solution to generate the most informative tweets for its users. By considering the novelty and popularity of(More)
This paper addresses the problem of mining users' interest from the vast, noise, unstructured and dynamic data generated on social media sites, taking Twitter as case study. The mining process uses different Natural Language Processing techniques to extract the relevant words from subscribers' tweets and applies cluster analysis over them. We evaluate the(More)