Abstract: Aiming at the properties of sparse feature, content fragmentation for microblog data, a hot topic detection method was proposed based on meaningful string clustering. The multiple strategies including repeated string detection, context analysis and language rule filtering were combined to extract meaningful strings. Candidate topics were generated by clustering with distribution of meaningful strings in documents. The hot topics were detected according to hotness sorting for candidate topics. As is shown from the experiment results on microblog data, the method achieves good effect in solving the problem of data sparseness. It is effective and feasible to hot topic detection for microblog.