A Novel Approach for Spam Rectification using Pattern Recognition

The impact of social spam is already significant. A social spam message is potentially seen by all the followers and recipients' friends. Even worse, it might cause misdirection and misunderstanding in public and trending topic discussions. Identifying these spammers and spam content is a very important issue. In the Existing System, Net Spam, the spam detection was done using Convolution Neural Network Algorithm. The possibility that anyone can leave a review offers a spam on spam reviews about products and services. Features for the detection of spammers could be user based or content based or both and spam classifier methods. In the Proposed System, social spam detection is done which can be used by any social network to detect spam. Once a new type of spam is detected on one network, the pattern can automatically be identified using K-Nearest Neighbor Algorithm. In addition, new social networks can quickly protect their users from social spam. By using content-based filtering algorithm, filtering of feature based reviews and negative spam reviews, prevention of fake reviews and detection of spam users based on the user's reviews is done.