In this paper, we propose an $\ell_1$-norm Symmetric Nonnegative Matrix Tri-Factorization ($\ell_1$ S-NMTF) framework to cluster multi-type relational data by utilizing their interrelatedness. Due to introducing the $\ell_1$-norm distances in our new objective function, the proposed approach is robust against noise and outliers, which are inherent in multi-relational data. We also derive the solution algorithm and rigorously analyze its correctness and convergence. The promising experimental results of the algorithm applied to text clustering on IMDB dataset validate the proposed approach.