“We are creating the same quantity of data every two days, as we created from the dawn of time up until 2003. It is estimated to be 5 Exabyte” . The Internet and web technologies give billions of users the ability to share information and express their opinions on various issues. This enormous amount of data might be very valuable. Social media, as the main sharing platform, is a very promising data source for researchers to investigate and analyze how people feel or think on variety of issues, from politics to entertainment. Previous research has explored the problem of detecting controversies involving multiple kinds of entities (people, event, ) by analyzing different feelings and opinions on these entities. The music domain, as one of the most controversial domains, has not been investigated much in this research. This thesis studies to which extent Twitter, as a social media platform, can be used to detect controversies involving music artists. It generalizes and extends the work proposed in previous research to build good machine learning prediction models to detect these controversies. We analyze what people share about music artists in Twitter, present the problems in this data and study how to tackle most of them. Then, we use this data to build a new controversy detection dataset in the music domain. The created dataset is then used to evaluate a comprehensive set of features to be used in building prediction models to detect controversies involving music artists. We propose using information about the users who share their opinions along with information about the shared opinions themselves to enrich this set of features. Our evaluations show promising results in detecting controversies involving music artists using the created dataset. They also show that we can easily improve the results of detecting controversies in other domains as we also run our evaluations on a CNN news dataset.