Exploiting Online Newspaper Articles Metadata for Profiling City Areas

Abstract

News websites are among the most popular sources from which internet users read news articles. Such articles are often freely available and updated very frequently. Apart from the description of the specific news, these articles often contain metadata that can be automatically extracted and analyzed using data mining and machine learning techniques. In this work, we discuss how online news articles can be integrated as a further source of information in a framework for profiling city areas. We present some preliminary results considering online news articles related to the city of Rome. We characterize the different areas of Rome in terms of criminality, events, services, urban problems, decay and accidents. Profiles are identified using the k-means clustering algorithm. In order to offer better services to citizens and visitors, the profiles of the city areas may be a useful support for the decision making process of local administrations.