Real-time monitoring of Twitter traffic by using semantic networks

Data from Social Networks and microblogs can provide useful information for prevention and investigation purposes, provided unstructured information is processed at both the lexical and the semantic level. The proposed methodology introduces a comprehensive Semantic Network (ConceptNet) in the interpretation chain of Twitter traffic. This additional interpretation level greatly enhances the effectiveness of semi-automated tools for monitoring purposes. In particular, the paper shows that the combined use of semantic and text-mining clustering tools also allows law-enforcement operators to early detect and track unscheduled events. Experimental results demonstrate the method effectiveness in real cases.