FRAUD DETECTION USING REDDIT RANKING ALGORITHM

Traditional methods of data analysis have long been used to detect fake reviews. Early data analysis techniques were oriented toward extracting quantitative and statistical data characteristics. Some of these techniques facilitate useful data interpretations and can help to get better insights into the process behind data. To go beyond a traditional system, a data analysis system has to be equipped with considerable amount of background data, and be able to perform reasoning tasks involving that data. In effort to meet this goal researchers have turned to the fields of machine learning and artificial intelligence. A review can be classified as either fake or genuine either by using supervised and/or unsupervised learning techniques. These methods seek reviewer's profile, review data and activity of the reviewer on the Internet mostly using cookies by generating user profiles. Using either supervised or unsupervised method gives us only an indication of fraud probability. In the proposed system here we are introducing a cross combined technology for Reddit Ranking Algorithm which comes under opinion mining category. Here we use Advanced Text categorization (ATC) with artificial neural network (ANN). We propose a deep data analysis model to identify fake users. This can be identified by using Ranking Process. Each and every comment will be categorized in this category. After the categorization, used count will be taken from the number of comments. In case of the user comments are around the range the user's rating will be accepted. Else the user will be blacklisted and the user's review will be removed from the rating list. This makes the system work perfectly with fine actual reviews.