Abstract As tweets have become a comprehensive repository of fresh information, Semantic Role Labeling (SRL) for tweets has aroused great research interests because of its central role in a wide range of tweet related studies such as ?ne-grained information extraction, sentiment analysis and summarization. However, the fact that a tweet is often too short and informal to provide suf?cient information poses a major challenge. To tackle this challenge, we propose a new method to collectively label similar tweets. The underlying idea is to exploit similar tweets to make up for the lack of information in a tweet. Speci?cally, similar tweets are ?rst grouped together by clustering. Then for each cluster a two-stage labeling is conducted: One labeler conducts SRL to get statistical information, such as the predicate/argument/role triples that occur frequently, from its highly con?dently labeled results; then in the second stage, another labeler performs SRL with such statistical information to re?ne the results. Experimental results on a human annotated dataset show that our approach remarkably improves SRL by 3.1% F1.