Guest Editorial: Special issue on software engineering for mobile applications

Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch

Detecting Fake Reviews: Just a Matter of Data

Along with the ever-increasing portfolio of products online, the incentive for market participants to write fake reviews to gain a competitive edge has increased as well. This article demonstrates the effectiveness of using different combinations of spam detection features to detect fake reviews other than the review-based features typically used. Using a spectrum of feature sets offers greater accuracy in identifying fake reviews than using review-based features only, and using a machine learning algorithm for classification and different amounts of feature sets further elucidates the difference in performance. Results compared by benchmarking show that applying a technique prioritizing feature importance benefits from prioritizing features from multiple feature sets and that creating feature sets based on reviews, reviewers and product data can achieve the greatest accuracy

伏在するサイバー攻撃の発見: 機械学習によるアプローチ

早大学位記番号:新7796早稲田大

Detecting Incentivized Review Groups With Co-Review Graph

Online reviews play a crucial role in the ecosystem of nowadays business (especially e-commerce platforms), and have become the primary source of consumer opinions. To manipulate consumers’ opinions, some sellers of e-commerce platforms outsource opinion spamming with incentives (e.g., free products) in exchange for incentivized reviews. As incentives, by nature, are likely to drive more biased reviews or even fake reviews. Despite e-commerce platforms such as Amazon have taken initiatives to squash the incentivized review practice, sellers turn to various social networking platforms (e.g., Facebook) to outsource the incentivized reviews. The aggregation of sellers who request incentivized reviews and reviewers who seek incentives forms incentivized review groups. In this paper, we focus on the incentivized review groups in e-commerce platforms. We perform the data collections from various social networking platforms, including Facebook, WeChat, and Douban. A measurement study of incentivized review groups is conducted with regards to group members, group activities, and products. To identify the incentivized review groups, we propose a new detection approach based on co-review graphs. Specifically, we employ the community detection method to find the suspicious communities from co-review graphs. We also build a “gold standard” dataset from the data we collected, which contains the information of reviewers who belong to incentivized review groups. We utilize the “gold standard” dataset to evaluate the effectiveness of our detection approach