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Social media represent an inexhaustible source of information concerning public places (also called points of interest (POIs)), provided by users. Several social media own and publish huge and independently-built corpora of data about public places which are not linked each other. An aggregated view of information concerning the same public place could be extremely useful, but social media are not immutable sources, thus the off-line approach adopted in all previous research works cannot provide up-to-date information in real time. In this work, we address the problem of on-line aggregating geo-located descriptors of public places provided by social media. The on-line approach makes impossible to adopt machine-learning (classification) techniques, trained on previously gathered data sets. We overcome the problem by adopting an approach based on fuzzy logic: we define a binary fuzzy relation, whose on-line evaluation allows for deciding if two public-place descriptors coming from different social media actually describe the same public place. We tested our technique on three data sets, describing public places in Manchester (UK), Genoa (Italy) and Stuttgart (Germany); the comparison with the off-line classification technique called “random forest” proved that our on-line technique obtains comparable results.

A Fuzzy Technique for On-Line Aggregation of POIs from Social Media: Definition and Comparison with Off-Line Random-Forest Classifiers