Regressing Controversy of Music Artists from Microblogs

Social media represents a valuable data source for researchers to analyze how people feel about a variety of topics, from politics to products to entertainment. This paper addresses the detection of controversies involving music artists, based on microblogs. In particular, we develop a new controversy detection dataset consisting of 53,441 tweets related to 95 music artists, and we devise and evaluate a comprehensive set of user-and content-based feature candidates to regress controversy. The evaluation results show a strong performance of the presented approach in the controversy detection task: F1 score of 0.811 in a classification task and RMSE of 0.688 in a regression task, using controversy scores in the range [1, 4]. In addition, the results obtained in applying the presented approach on a dataset from a different domain (CNN news controversy) demonstrate transferability of the developed feature set, with a significant improvement over prior approaches. A combination of the adopted Gradient Boosting based classifier and the developed feature set results in an F1 score of 0.775, which represents an improvement of 9.8% compared to the best prior result on this dataset.