Unstructured Arabic text documents are an important source of geographical and temporal information. The possibility of automatically tracking spatio-temporal information, capturing changes relating to events from text documents, is a new challenge in the fields of geographic information retrieval (GIR), temporal information retrieval (TIR) and natural language processing (NLP). There was a lot of work on the extraction of information in other languages

Recently, Twitter as one of social networks has been considered as a rich source of spatio-temporal information and significant revenue for mining data. Event detection from tweets can help to predict more serious real-world events. Such as: criminal events, natural hazards, and the spread of epidemics. Etc. This paper deals with event-based extraction for criminal incidents from Arabic tweets. It presents a framework that supports automated extraction of spatial and temporal information from tweets. The proposed approach is based on combining various indicators, including the names of places and temporal expressions that appear in the tweet message, related tweeting time, and additional locations from the user's profile. The effectiveness of the system was evaluated in term of recall, precision and f-measure.