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Twitter has the fast propagation and diffusion of information compare to other SNS. Therefore, many researches about detecting real-time event using twitter are progressing. Twitter real-time event detecting system assumes every twitter user as a sensor and analyzes their written tweet in order to detect the event. Researches that are related to this twitter have already obtained good results but confronted the limits because of some problems. Especially, many existing researches are using the method that can trace an event location by using GPS coordinate. However, it can be suggested a definite limitation through the present user's skeptical responses about making personal location information public. Therefore, this paper suggests the method that traces the location information in tweet contents text without using the provided location information from twitter. Associated words were grouped by using the keyword that extracted in tweet contents text. The place that the events have occurred and whether the events have surely occurred are detected by this experiment using this algorithm. Furthermore, this experiment demonstrated the necessity of the suggested methods by showing faster detection compare to the other existing media.

While SNS services such as Twitter or Facebook are rapidly growing, research for the SNS analysis has been concerned. Especially, twitter reacts to social issues in real-time so that it is used to get useful experimental data for researchers of social science or information retrieval. However, it is still lack of research on the methodology to collect data. Therefore, this paper suggests the tweet acquisition system by considering tendency of twitter user oriented location-based event and political·social event. First the system acquires tweets including information of location and keyword about event and secure IDs for acquisition of political·social event. Then we plan ID-analyzer to classify the tendency of users. In addition for measuring reliability of ID-analyzer, it acquires and analyzes the tweet by using high-ranked ID. In analyses result, top-ranked ID shows 88.8% reliability, 2nd-ranked ID shows 76.05% and ID-analyzer shows 77.5%, it shortens collection time by using minority ID.Keywords:Social Network Analysis, Tendency Analysis, Data Acquisition, Information Retrieval