A New Mashup Based Method for Event Detection from Social Media

摘要

Some events, such as terrorism attacks, earthquakes, and other events that represent tipping points, remain engraved in our memories. Today, through social media, researchers attempt to propose approaches for event detection. However, they are confronted to certain challenges owing to the noise of data propagated throughout social media. In this paper, a new mashup based method for event detection from social media is proposed using hadoop framework. The suggested approach aims at detecting real-world events by exploiting data collected from different social media sites. Indeed, the detected events are characterized by such descriptive dimensions as topic, time and location. Moreover, our approach assures a bilingual event detection. In fact, the proposed approach is able to detect events in English and French languages. In addition, our approach provides a mashup based multidimensional visualization by combining different multimedia components so as to add more details to the detected events. Furthermore, in order to overcome the problems occurring from the processing of big data, we integrated our approach into the hadoop distributed system.