Biblio

Today, the online review system cannot fully support the business since there are fraudulent activities inside. The companies that get low score reviews are induced to raise their score for the market competition capability by paying to the platform for deleting or editing the posted reviews. Moreover, the automatic filtration system of a platform removes some reviews without the awareness of the users. The low transparency platform causes low credibility toward the reviews. Blockchain technology provides exceptionally high transparency since every action can be traced publicly. However, there are some tradeoffs that need to be considered, such as cost and response time. This work tends to find the potential of using Blockchain technology in the online review system by testing four implementation approaches of the Ethereum Smart Contract. The result illustrates that using IPFS to store the data is a practical way of reducing transaction costs. Besides, preventing using Smart Contract states can significantly reduce costs too. The response time for using the Blockchain and IPFS system is slower than the centralized system. However, posting a review does not need a fast response. Thus, it is worthy of trading response time with transparency and cost. In the business view, the review posting with cost causes more difficulty to generate fake reviews. Moreover, there are other advantages over the centralized system, such as the reward system, bogus review voting, and global database. Thus, credibility improvement for a consumer online review system is a potential application of Blockchain technology.

Nowadays, online reviews have become one of the vital elements for customers to do online shopping. Organizations and individuals use this information to buy the right products and make business decisions. This has influenced the spammers or unethical business people to create false reviews and promote their products to out-beat competitions. Sophisticated systems are developed by spammers to create bulk of spam reviews in any websites within hours. To tackle this problem, studies have been conducted to formulate effective ways to detect the spam reviews. Various spam detection methods have been introduced in which most of them extracts meaningful features from the text or used machine learning techniques. These approaches gave little importance on extracted feature type and processing rate. NetSpam[1] defines a framework which can classify the review dataset based on spam features and maps them to a spam detection procedure which performs better than previous works in predictive accuracy. In this work, a method is proposed that can improve the processing rate by applying a distributed approach on review dataset using MapReduce feature. Parallel programming concept using MapReduce is used for processing big data in Hadoop. The solution involves parallelising the algorithm defined in NetSpam and it defines a spam detection procedure with better predictive accuracy and processing rate.