A study on topics identification on Twitter using clustering algorithms

The identification of topics in Social Networks has become an important research task when dealing with event detection, particularly when global communities are affected. Text processing techniques and machine learning algorithms have been extensively used to solve this problem. In this paper we compare three clustering algorithms - k-means, k-medoids and NMF (Non-negative Matrix Factorization) - in order to detect topics related to textual messages obtained from Twitter. The algorithms were applied to a database composed by tweets, having as initial context hashtags that are related to the recent scandal of corruption involving FIFA (International Federation of Football Association). Obtained results suggest that the NMF presents better results, since it provides providing clusters that are easier to interpret.