Sandra Servia Rodríguez

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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)
Many current Internet services rely on inferences from models trained on user data. Commonly, both the training and inference tasks are carried out using cloud resources fed by personal data collected at scale from users. Holding and using such large collections of personal data in the cloud creates privacy risks to the data subjects, but is currently(More)
Measuring mental well-being with mobile sensing has been an increasingly active research topic. Pervasiveness of smartphones combined with the convenience of mobile app distribution platforms (e.g., Google Play) provide a tremendous opportunity to reach out to millions of users. However, the studies at the confluence of mental health and mobile sensing have(More)
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)