Geo-FUZZ: Fuzzy-based algorithm for suspicious geo-tagged tweets detection

Meryem Bendella, Feda Almuhisen, Mohamed Quafafou

Abstract: Social media such as Twitter is becoming an increasingly incredible source for capturing and analyzing users' conversations. However, information published by users may contain dangerous contents and give negative influence to other users. In this paper, we proposed an algorithm for detecting suspicious tweets based on fuzzy logic and probabilistic methods from geo-tagged tweets. The novelty of our work is by considering tweets location, labeling regions and classifying tweets based not only on text contents but also on the region where the message is posted. Moreover, this method generates a geo-tagged map to easily visualize the classified tweets. The experimental results show that the Geo-FUZZ proposed algorithm is more accurate compared to the previous algorithm FUZZ-STD. Furthermore, the visualization results of hot-suspicious zone allow us to identify risk early to decrease its impact, and control the spread of abnormal similar tweet for security.