Abstract
Consider an urban environment and its semi-public realms (e.g., shops, bars, visitors attractions, means of transportation). Who is the maven of a district? How fast and how broad can such maven influence the opinions of others? These are just few of the questions BOTTARI (our Location-based Social Media Analysis mobile app) is getting ready to answer. In this position paper, we recap our investigation on deductive and inductive stream reasoning for social media analysis, and we show how the results of this research form the underpinning of BOTTARI. © 2012 Springer-Verlag.
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CITATION STYLE
Celino, I., Dell’Aglio, D., Della Valle, E., Huang, Y., Lee, T., Kim, S. H., & Tresp, V. (2012). Towards BOTTARI: Using stream reasoning to make sense of location-based micro-posts. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7117 LNCS, pp. 80–87). https://doi.org/10.1007/978-3-642-25953-1_7
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