The quantity of unstructured and structured data from social networking platforms and mobile phone instant messaging applications is massive and is produced at an exponential rate yet there is no mechanism to verify the content's truthfulness and trustworthiness. In this paper we have proposed a theory of how Big Data technology can be employed to validate the credibility of the vastly diffused data. Using technologies like Hadoop to analytically process the fast paced incoming data and measure the reliability of the content. To verify the integrity, the processed data must be inspected against entrusted sources. These sources must be accessible and should have trustworthy data that can assist in measuring the authenticity of contents from various sources. While privacy concerns are often dismissed when data is scraped from public-facing platforms such as Facebook, the need for these sites to validate the data posted on their site becomes prudent. Posting false rumors devalues the extent to which social networking acts as an effective method of spreading true information. In this paper we provide a brief exploration on Big Data, Hadoop and influence of unsolicited messages propagated from social networking websites and instant messaging applications.