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

TWC：小型：利用语言和行为线索进行意见垃圾邮件检测的统计模型

• 批准号: 1527364
• 负责人: Arjun Mukherjee
• 金额: $ 49.97万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527364&HistoricalAwards=false
• 关键词: TWC; Small; Statistical; Models; Opinion

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.

如今，在线意见在决策中发挥着关键作用，并影响着我们生活的方方面面。吃饭的餐厅、住宿的地方、就读的大学、阅读的书籍、咨询的医生，甚至投票的政治候选人的选择，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。由于美国经济的很大一部分正在受到社会舆论的驱动，这给公众带来了严重的风险（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将网络欺诈视为非法和法律犯罪。检测虚假的在线意见是一个紧迫的研究领域。否则，在线社交媒体可能会继续发展而不被发现。该项目旨在开发新颖的欺骗检测算法以识别欺诈行为。它协同整合了计算语言学、行为建模和统计机器学习的技术，以推进该领域的知识发展。该项目包括四方面的研究工作：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）将评论者的“垃圾性”视为潜在的观察到的行为足迹的无监督模型，3）利用固有强化关系对评论、评论者及其语言和行为模式进行联合建模的关系架构， 4）融合所有方法线索的集成评分机制和端到端验证框架。该项目开发的技术可以（1）降低电子商务中的营销、消费者和经济风险； (2) 改进用户分析，检测与国家安全有重大关系的在线骚扰、偏执、巨魔和其他社交媒体欺诈行为； (3) 开发课程/教程的过渡技术，吸引代表性不足的学生，包括少数族裔和女性。其结果是一套新颖的、有原则的、可扩展的技术来大规模过滤垃圾评论。