SEMANTIC UPLIFT CRIMINAL DATA FROM SOCIAL NETWORKS

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The evolution of web technologies brought a new view over Web published data, making the information available to both humans and machines. However, as societies are built over citizens’ needs, it is difficult to determine how governments are performing on addressing these needs. Social networks are an interesting tool for people to express their aspirations, but monitoring peoples’ activities to understand their behaviour is a difficult task. This paper presents an application of the semantic uplift technique on social networks to store violence and criminality data. The procedure uses the output of natural language processing techniques to classify criminal activity in a general taxonomy, so data can be compared to worldwide statistics.

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