Spammer Detection And Fake User Identification on Social Media

Data Mining

As e-commerce is growing and becoming popular day-by-day, the number of reviews received from customer about any product grows rapidly. People nowadays heavily rely on reviews before buying anything. Product reviews play an important role in deciding the sale of a particular product on the ecommerce websites or applications like Flipkart, Amazon, Snapdeal, etc. In this paper, we propose a framework to detect fake product reviews or spam reviews by using Opinion Mining. The Opinion mining is also known as Sentiment Analysis. In sentiment analysis, we try to figure out the opinion of a customer through a piece of text. The proposed method called VWNB-FIUT (Value Weighted Naïve Bayes with Frequent Pattern Ultra Metric Tree) automatically classifies users' reviews into "suspicious", "clear" and "hazy" categories by phase-wise processing. The hazy category recursively eliminates elements into suspicious or clear. This results into richer detection and be useful to business organization as well as to customers. Business organization can monitor their product selling by analyzing and understanding what the customers are saying about products. This can help customers to purchase valuable product and spend their money on quality products. Finally end users see that each individual review with polarity scores and credibility score annotated on it. We first take the review and check if the review is related to the specific product with the help of VWNB. We use Spam dictionary to identify the spam words in the reviews by using FIUT. In Text Mining we apply several algorithms and on the basis of these algorithms we get the specific results.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.