Social smart city: A platform to analyze social streams in smart city initiatives

Abstract

A central issue in the context of smart cities is how to analyze a large amount of data generated by different kinds of sources in real time. This paper reports a case study in real-time acquisition of crime detection information from social media messages, built on top of a plataform for fast processing and visualization of data from Twitter. The purpose is to allow city managers to act timely on preventing crime occurence as detected from tweets posted by real users. Key issues here are the processing of a large volume of data and modularization and customization capabilities implemented through pipelined modules for robust, fast, real time tweet acquisition and storage. In particular, the customization is reflected by modules of filtering of several kinds, natural language processing tasks, topped by a machime learning analysis that allows for the classification of the input messages according to the local policy category system.