The online social networking service is a stage to build social relation among user who share life events. Twitter is one such social network which has been embellished in last decade. It provides a social platform for users to tweet and retweet. It consent user to share connections, naturalistic subjects such as entropy, upshot, tidings. Nowadays twitter is transforming into a rostrum for diffusion rumors. Limiting and restraining the dispersal of rumors is a critical issue. The post propagates in social network are stratified as genuine and not genuine. Two ways 1) Obstructing the misinformation 2) Discern the source for rumor are handled. To limit the rumor propagation in social network series of factual analysis are carried out. First, a social networking site is simulated and each registered user are assigned with trustee rate. Second, sentiment analysis is engaged in categorizing the post shared by users. Third, keywords are extracted as themes using natural language processing. The themes extracted are used to find the correlations between tweets. The threshold rate of the post is examined to obstruct the misinformation. Currently, paper focus on limiting the propagation of rumor in social network. Results unveil that misinformation diffusion can be restrained and source can be detached from network and this provides a trustworthy events.