Evaluation the spread of misinformation, disinformation and propaganda in online social networks. Analyzing online social networks to identify metrics to infer cues of deception will enable us to measure diffusion of misinformation. An algorithm to effectively detect deliberate spread of false information which would enable users to make informed decisions while spreading information in social networks. The computationally efficient algorithm uses the collaborative filtering property of social networks to measure the credibility of sources of information as well as quality of news items. The acceptance of misinformation or misleading information by the people depends on their prior beliefs and opinions. People believe things which support their prior thoughts without questioning them. Decision making out of ignorance is often based on heuristics and the level of confidence on the decision is also low, making correction easier. Such decisions are often correct and are generally not catastrophic. False beliefs based on misinformation are held strongly and often result in greater support for a cause. Such beliefs are also very contagious and the person makes efforts to spread them to others. The spread of misinformation is a result of a cognitive process by the receivers based on their assessment of the truth value of information. Acceptance of information is more the norm than otherwise for most of the people. When people evaluate the truth value of any information they take into account four factors. τ-Monitor Placement problem and prove its #P-complete hardness is used for placement of monitor set.