Pairwise constraints, which specifies whether a pair of samples belong to the same class (must-link constraints) or different classes (cannot-link constraints). In this paper, we propose a new feature selection algorithm based on pairwise constraints for linked social media data, which integrate the linking information and pairwise constraints simultaneously to select robust features. As it known to all, the wide use of social media produces massive, high-dimensional and unlabeled social media data. Feature selection has been shown effective in dealing with high-dimensional data. The experiments on Flickr and BlogCatalog datasets evaluate that our method outperforms other conventional feature selection methods.