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

考虑到Yelp、Amazon和TripAdvisor等网站上的用户评论，我们应该信任哪一个？网络评论已成为舆论分享的重要资源。它们影响着我们在日常和专业活动的方方面面的决定：例如，去哪里吃饭，住在哪里，买什么产品，看什么医生，读什么书，上哪所大学，等等。然而，在线评论的可信性和可信赖性正处于危险之中。众所周知，大量的评论是捏造的——要么是由业主、竞争对手，要么是由他们付钱的实体——以制造对产品和服务实际质量的错误看法。更重要的是，舆论欺诈很普遍；虽然信用卡欺诈的发生率仅为0.2%或更低，但据估计，知名服务网站上20-30%的评论可能是虚假的。这给企业和公众带来了严重的风险，从投资于低质量的产品到咨询不称职的医生进行诊断和治疗。与其他类型的欺诈一样，意见欺诈是一种严重的法律犯罪。事实上，政策制定者目前认为这是执法中的一个严重问题。因此，解决这一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，保持其服务的完整性，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。企业也将受益于具有可靠反馈的评论。诚实的企业将间接得到奖励，因为不道德的企业将不再容易从虚假评论中获益。研究成果将为互联网商务的健康发展做出重要贡献。教育活动包括将研究成果纳入研究生课程，教育公众了解欺诈行为和错误信息，并提供包括讲座和手稿在内的公开教育材料。考虑到网络社区的意见欺诈问题，人们如何识别虚假评论并确定其背后的罪魁祸首？通过结合pi在语言、用户行为和关系信息等各种欺骗足迹模式上的专业知识，该项目提出了一个研究计划，将为这一新兴的、普遍的、具有社会影响力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，创建一个统一的检测框架；从语言学，用户行为，和网络效应，以获得最好的所有世界。主要思想是将问题表述为复合异构网络上的关系推理任务，提供一种原则性的、可扩展的方法，可以混合和加强上述所有线索，以实现有效和健壮的欺诈检测。从科学的角度来看，该研究汇集了三个学科：自然语言分析、行为建模和图挖掘。结果是一套新颖的、有原则的、可扩展的技术和模型，这将增强我们对大规模舆论欺诈和错误信息的产生和传播的理解。pi将与Yelp、b谷歌和亚马逊等行业合作伙伴合作，直接征求在线虚假评论，并进行精心设计的用户研究，以测试和验证他们的技术。该项目的网站（http://www.andrew.cmu.edu/user/lakoglu/PROJECTS/OPINION_FRAUD/）提供了更多信息，并将包括开源软件和数据集。

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.