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Social media has gained impressive popularity all around the world in the last decade. Social networks such as Twitter, Facebook, LinkedIn, and Instagram have acquired their user’s attraction by maintaining their identity with very similar features. With the popularity of these platforms, now a day most of the users tend to rely on the information published on social media. Therefore, the credibility of social media information is playing a major role in the present cyberspace. As an example, the Twitter platform is handling 500 million tweets per day. Most of the twitter messages are truthful, but the twitter platform is also used to spread rumors and misinformation. Truthfulness or reliability is depending on the source's credibility. Twitter profiles can be identified as the information source on the twitter platform. In this paper, a user reputationbased prediction method is proposed to analyze the twitter source credibility. The proposed solution is mainly based on the k-means clustering model. Another two models namely, news category analysis and sentiment analysis are deployed to generate novel features for the clustering method. The objective of this paper is to introduce a credibility rating method to visualize the user credibility of twitter user profiles. So that followers can have an understanding about the trustworthiness of the information published on that profile. Producing the agreement score for a specific twitter user is one of a novel experiment in this research. Achieved accuracy by the system is 0.68 according to the evaluations conducted.

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