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

考虑到Yelp、亚马逊和TripAdvisor等网站上的用户评论，人们应该相信哪一个？网络评论已成为民意分享的重要资源。它们影响着我们在日常和专业活动中的广泛决策：例如，在哪里吃、在哪里住、买什么产品、看什么医生、读什么书、上什么大学等等，然而，网络评论的公信力和可信赖度却岌岌可危。众所周知，大量的评论是由所有者、竞争对手或由这些人支付的实体捏造的，目的是对产品和服务的实际质量造成错误的看法。更重要的是，意见欺诈是普遍的;虽然信用卡欺诈是罕见的0.2%或更少，据估计，20-30%的评论知名服务网站可能是假的。这给企业和公众带来了严重的风险，从投资低质量的产品到咨询不称职的医生进行诊断和治疗。像其他类型的欺诈一样，意见欺诈是一种严重的法律的罪行。事实上，决策者目前已认识到这是执法中的一个严重问题。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，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。企业也将受益于具有可靠反馈的评论。诚实的企业将间接获得奖励，因为无良企业将不再容易从虚假评论中受益。因此，研究成果将有助于互联网商务的健康发展。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，并提供公开的教育材料，包括讲座和手稿。鉴于在线社区中的意见欺诈问题至关重要，如何识别虚假评论并将其归咎于背后的罪魁祸首？通过结合专业知识的PI在各种形式的欺骗足迹，包括语言，用户行为和关系信息，该项目提出了一个研究计划，将导致急需的解决方案，这个紧急的，普遍的，社会影响力的问题。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。其主要思想是将问题制定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，可以混合和加强所有上述线索，以实现有效和强大的欺诈检测。从科学的角度来看，该研究汇集了三个学科：自然语言分析，行为建模和图挖掘。其结果是一套新颖的，原则性的，可扩展的技术和模型，将提高我们的理解的创造和传播的意见欺诈和错误信息一般在大规模。PI将与Yelp、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，以测试和验证其技术。项目网站（http：//www.andrew.cmu.edu/user/lakoglu/PROJECTS/OPINION_FRAUD/）提供更多信息，并将包括开放源码软件和数据集。

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.