Abstract
Social networks are being used by terrorist organizations to distribute messages with the intention of influencing people and recruiting new members. The research presented in this paper focuses on the analysis of Twitter messages to detect the leaders orchestrating terrorist networks and their followers. A big data architecture is proposed to analyze messages in real time in order to classify users according to different parameters like level of activity, the ability to influence other users, and the contents of their messages. Graphs have been used to analyze how the messages propagate through the network, and this involves a study of the followers based on retweets and general impact on other users. Then, fuzzy clustering techniques were used to classify users in profiles, with the advantage over other classifications techniques of providing a probability for each profile instead of a binary categorization. Algorithms were tested using public database from Kaggle and other Twitter extraction techniques. The resulting profiles detected automatically by the system were manually analyzed, and the parameters that describe each profile correspond to the type of information that any expert may expect. Future applications are not limited to detecting terrorist activism. Human resources departments can apply the power of profile identification to automatically classify candidates, security teams can detect undesirable clients in the financial or insurance sectors, and immigration officers can extract additional insights with these techniques.
Highlights
Social networks are playing a very important role in the way people think
Sentiment analysis to detect radicalization has been applied to social networks in the past as an evolution of previous analysis that were traditionally focused on websites and forums [2]
In this paper the fuzzy clustering method takes as an input the results obtained from the graph analysis, along with some characteristics directly extracted from the social network
Summary
Social networks are playing a very important role in the way people think. When accurately targeted, repeated messages can reinforce political ideas or even flip the way of thinking of the most indecisive. A very challenging part of the analysis presented in this paper is how to measure the impact of each user in the network, as it depends on the volume of tweets (activity) combined with the number of followers but is amplified by the number of retweets. For this purpose, a deep analysis is carried out using graphs. Problem is more related to ambiguity as a result of unreliable data or contradictory terms in the messages In this case advanced text mining or natural language processing techniques would be appropriate [8,9,10]. In this paper the fuzzy clustering method takes as an input the results obtained from the graph analysis, along with some characteristics directly extracted from the social network
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
