Event propagation analysis is one of the main research issues in the field of social network analysis. Hotspot outbreaks and spreads through the social network, and it makes a great impact in a short period of time. Meanwhile, it is easier to create a hotspot and spread it in social network than in traditional media, so information diffusion will do harm to social security and property if used by criminals. Traditional influence propagation analysis method can only analyze single microblog (or tweet), so it limits event propagation analysis in social network. In this paper, we review some existing propagation models such as independent cascade model, linear threshold model, etc. After that, we introduce some basic definitions of influence propagation analysis in social network. Then we propose a method combining user deduplication, spammer detection and probabilistic reading based on existing independent cascade model. The main idea of our method is making user deduplication in the event composed of several key microblogs (or tweets) and building event propagation graph. Then we remove spammers in that graph and make influence propagation analysis by using probabilistic reading model. It provides a novel method to make event propagation analysis. Finally, some experiments are conducted and the results demonstrate the correctness and effectiveness of the method.