III: Medium: Collaborative Research: Collective Opinion Fraud Detection: Identifying and Integrating Cues from Language, Behavior, and Networks

III：媒介：协作研究：集体意见欺诈检测：识别和整合来自语言、行为和网络的线索

• 批准号: 1408924
• 负责人: Christos Faloutsos
• 金额: $ 29.99万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1408924&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Collective

Given user reviews on Web sites such as Yelp, Amazon, and TripAdvisor, which ones should one trust? Online reviews have become an important resource for public opinion sharing. They influence our decisions over an extremely wide spectrum of daily and professional activities: e.g., where to eat, where to stay, which products to purchase, which doctors to see, which books to read, which universities to attend, and so on. However, the credibility and trustworthiness of online reviews are at stake. It is well known that a large body of reviews is fabricated -- either by owners, competitors, or entities paid by those -- to create false perception on the actual quality of the products and services. What is more, opinion fraud is prevalent; while credit card fraud is as rare as 0.2% or less, it is estimated that 20-30% of the reviews on well-known service sites could be fake. This poses a serious risk to businesses and the public, from investing on a low-quality product to consulting an incompetent doctor for diagnosis and treatment. Like other kinds of fraud, opinion fraud is a serious legal offense. In fact, it is currently being recognized as a serious issue in law enforcement by policymakers. Thus solving this problem is of great importance to businesses and the general public alike. Accurately spotting opinion fraud will enable site owners to provide trustworthy content, maintain the integrity of their service, and protect the online citizens from unfair (or potentially harmful) products and services. Businesses will also benefit from reviews with reliable feedback. Honest businesses will be indirectly rewarded, as it will no longer be easy for unscrupulous businesses to benefit from fake reviews. The research outcomes will thus contribute significantly to the healthy growth of the Internet commerce. Educational activities include incorporating research findings in graduate level courses, educating public on fraudulent behavior and misinformation, and providing publicly available educational materials including lectures and manuscripts.Given the critical issues of opinion fraud in online communities, how can one identify fake reviews and attribute responsible culprits behind them? By conjoining expertise of the PIs over various modalities of deception footprints ranging over language, user behavior, and relational information, this project presents a research program that will result in much needed solutions to this emergent, prevalent, and socially impactful problem. The ultimate goal is to create a unified detection framework via synergistic integration of multiple information sources; from linguistics, user behavior, and network effects, to obtain the best of all worlds. The main idea is to formulate the problem as a relational inference task on composite heterogeneous networks, providing a principled, extensible approach that can blend and reinforce all the above cues towards effective and robust detection of fraud. From a scientific point of view, the research brings together three disciplines: natural language analysis, behavioral modeling, and graph mining. The outcome is a suite of novel, principled, and scalable techniques and models that will enhance our understanding of the creation and dissemination of opinion fraud and misinformation in general at a large scale. The PIs will collaborate with industry partners such as Yelp, Google, and Amazon, directly solicit online fake reviews, and conduct well-designed user studies for testing and validation of their techniques. The project web site (http://www.andrew.cmu.edu/user/lakoglu/PROJECTS/OPINION_FRAUD/) provides additional information and will include open-source software and datasets.

鉴于Yelp，Amazon和TripAdvisor等网站上的用户评论，应该信任哪些？在线评论已成为舆论共享的重要资源。它们在每日和专业活动中都会影响我们的决定：例如，在哪里吃饭，住在哪里，要购买的产品，哪些医生可以看哪些书籍，要读的书籍，要上的大学等等。但是，在线评论的可信度和可信度受到威胁。众所周知，由所有者，竞争对手或那些人支付的实体制造了大量的评论，以对产品和服务的实际质量产生虚假看法。更重要的是，意见欺诈是普遍的。虽然信用卡欺诈范围为0.2％或更少，但据估计，著名服务网站的评论中有20-30％可能是假的。这对企业和公众构成了严重的风险，从投资低质量的产品到咨询无能的医生进行诊断和治疗。像其他类型的欺诈一样，意见欺诈是严重的法律犯罪。实际上，该决策者目前在执法方面被认为是一个严重的问题。因此，解决这个问题对企业和公众都非常重要。准确地发现意见欺诈将使网站所有者能够提供值得信赖的内容，维持其服务的完整性，并保护在线公民免受不公平（或潜在有害的）产品和服务的侵害。企业还将从具有可靠反馈的评论中受益。诚实的企业将受到间接奖励，因为不道德的企业不再容易从虚假评论中受益。因此，研究结果将对互联网贸易的健康增长产生重大贡献。教育活动包括将研究结果纳入研究生级课程，向公众教育欺诈行为和错误信息，并提供公开可用的教育材料，包括讲座和手稿。在线社区中，人们如何识别虚假的评论和属性在他们身后负责罪魁祸首？通过结合PI的专业知识，涉及语言，用户行为和关系信息的各种欺骗足迹的方式，该项目提出了一项研究计划，该计划将为这种新兴，普遍和社会影响的问题提供急需的解决方案。最终目标是通过多个信息源的协同集成来创建一个统一的检测框架；从语言学，用户行为和网络效应中获得所有世界中最好的。主要思想是将问题提出为复合异质网络的关系推理任务，提供了一种有原则的，可扩展的方法，可以将上述所有线索融合在一起，以实现有效且强大的欺诈检测。从科学的角度来看，该研究汇集了三个学科：自然语言分析，行为建模和图形挖掘。结果是一套新颖，有原则和可扩展的技术和模型的套件，可以增强我们对观点欺诈和大规模宣传的创造和传播的理解。 PIS将与Yelp，Google和Amazon等行业合作伙伴合作，直接征集在线伪造评论，并进行精心设计的用户研究以测试和验证其技术。项目网站（http://www.andrew.cmu.edu/user/lakoglu/projects/opinion_fraud/）提供其他信息，并将包括开放式软件和数据集。