Feature selection is widely used in preparing high- dimensional data for effective data mining. Increasingly popular social media data presents new challenges to feature selection. Social media data consists of (1) tra- ditional high-dimensional, attribute-value data such as posts, tweets, comments, and images, and (2) linked data that describes the relationships between social me- dia users as well as who post the posts, etc. The nature of social media also determines that its data is mas- sive, noisy, and incomplete, which exacerbates the al- ready challenging problem of feature selection. In this paper, we illustrate the differences between attribute- value data and social media data, investigate if linked data can be exploited in a new feature selection frame- work by taking advantage of social science theories, ex- Tensively evaluate the effects of user-user and user-post relationships manifested in linked data on feature selec- Tion, and discuss some research issues for future work.