Spam detection of Twitter traffic: A framework based on random forests and non-uniform feature sampling
Claudia Meda, Edoardo Ragusa, Christian Gianoglio, Rodolfo Zunino, Augusto Ottaviano, Eugenio Scillia, Roberto Surlinelli · 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · 2016
Law Enforcement Agencies cover a crucial role in the analysis of open data and need effective techniques to filter troublesome information. In a real scenario, Law Enforcement Agencies analyze Social Networks, i.e. Twitter, monitoring events and profiling accounts. Unfortunately, between the huge amount of internet users, there are people that use microblogs for harassing other people or spreading malicious contents. Users' classification and spammers' identification is a useful technique for relieve Twitter traffic from uninformative content. This work proposes a framework that exploits a non-uniform feature sampling inside a gray box Machine Learning System, using a variant of the Random Forests Algorithm to identify spammers inside Twitter traffic. Experiments are made on a popular Twitter dataset and on a new dataset of Twitter users. The new provided Twitter dataset is made up of users labeled as spammers or legitimate users, described by 54 features. Experimental results demonstrate the effectiveness of enriched feature sampling method.