News clustering based on similarity analysis

This paper's focus is to continue the research on Internet psychological warfare analysis, where the authors faced a necessity to propose an accurate algorithm for news clustering that could be able to group news into semantically close sets. A two stage approach to reach that goal is proposed. First a similarity estimation between news messages is performed using semantic similarity metric based on WordNet. Next, the most suitable for given data structure clustering algorithms is selected in order to obtain thematic news clusters and observe their size distribution over time. Experiments were made on news volumes from several news mass media official pages in Facebook.