Abstract
This paper proposes a hybrid feature selection method for predicting user influence on Twitter. A set of candidate features from Twitter is identified based on the five attributes of influencers defined in sociology. Firstly, less relevant features are filtered out with a feature-weighting algorithm. Then the Sequential Backward Floating Selection is utilized as the search strategy. A Back Propagation Neural Network is employed to evaluate the feature subset at each step of searching. Finally, an optimal feature set is obtained for predicting user influence with a high degree of accuracy. Experimental results are provided based on a real world Twitter dataset including seven million tweets associated with 200 popular users. The proposed method can provide a set of features that could be used as a solid foundation for studying complicated user influence evaluation and prediction.
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