TWC: Small: Statistical Models for Opinion Spam Detection Leveraging Linguistic and Behavioral Cues

现在，网络观点在决策过程中起着举足轻重的作用，影响着我们生活的方方面面。就餐地点、住宿地点、就读大学、阅读书籍、咨询医生，甚至投票给政治候选人的选择，在很大程度上都受到众包意见的影响。然而，据估计，网站上高达30%的评论是虚假的。由于美国经济的更大部分正变得由社会舆论驱动，这对普通公众构成了严重风险(例如，被误导投资于低质量的产品、服务或医生)。联邦贸易委员会的意见可能很快就会认为网络欺诈是非法的，是合法的。检测虚假的网络评论是一个亟待研究的领域。否则，在线社交媒体可能会继续发展，而不会被发现。该项目旨在开发新的欺骗检测算法，以识别欺诈行为。它协同集成了计算语言学、行为建模和统计机器学习的技术，以促进这一领域的知识。该项目包括四个方面的研究工作：1)从大规模噪声人群数据和小规模领域专家编码数据中学习欺骗分类器的新方法；2)无监督模型，将评论者的“垃圾行为”视为潜在的观察到的行为足迹；3)利用固有的强化关系，联合建模评论者、评论者及其语言和行为模式的关系架构；以及4)融合所有方法的线索的集成评分机制，以及端到端的验证框架。该项目开发的技术可以(1)减少电子商务中的营销、消费者和经济风险；(2)改进用户概况，检测与国家安全密切相关的在线骚扰、偏执、巨魔和其他社交媒体欺诈；以及(3)将过渡技术开发为课程/教程，并吸引代表不足的学生，包括少数族裔和女性。其结果是一套新颖的、原则性的、可扩展的技术来大规模过滤观点垃圾邮件。

Online opinions now play a pivotal role in decision making and influence a wide spectrum of our lives. Choices of restaurants at which to dine, places to stay, universities to attend, books to read, doctors to consult, and even political candidates to vote for, are largely influenced by crowdsourced opinions. However, it is estimated that up to 30% of reviews on websites are fake. As a larger part of the US economy is becoming driven by social opinions, it poses a serious risk to the general public (e.g., by getting mislead to invest on low quality products, services or doctors). The Federal Trade Commission Opinion may soon consider online fraud as unlawful and a legal offense. Detecting fake online opinions is an urgent research area. Otherwise, online social media might continue to progress undetected. This project aims to develop novel deception detection algorithms in order to identify fraudulent behavior. It synergistically integrates techniques from computational linguistics, behavioral modeling and statistical machine learning in order to advance knowledge in this area. The project consists of a four-pronged research effort: 1) novel methods to learn deception classifiers from large-scale noisy crowd data and small-scale domain expert coded data, 2) unsupervised models that treat "spamicity" of reviewers as latent with observed behavioral footprints, 3) a relational architecture for jointly modeling reviews, reviewers, and their linguistic and behavioral patterns leveraging inherent reinforcement relations, and 4) an ensemble scoring mechanism blending cues from of all approaches, and an end-to-end validation framework. The techniques developed in the project can (1) reduce the marketing, consumer, and economic risk in e-commerce; (2) improve user profiling, detecting online harassment, bigotry, trolls, and other social media fraud that are of major relevance to national security; and (3) transition techniques developed to courses/tutorials and attract underrepresented students, including minorities and women. The result is a suite of novel, principled, and scalable techniques to filter opinion spam at large scale.