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: 1408287
• 负责人: Leman Akoglu
• 金额: $ 60万
• 依托单位: SUNY at Stony Brook
• 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 2017-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1408287&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.cs.stonybrook.edu/~leman/PROJECTS/OPINION_FRAUD/) provides additional information and will include open-source software and datasets.

鉴于Yelp、亚马逊和TripAdvisor等网站上的用户评论，人们应该信任哪些网站？网络评论已成为舆情分享的重要资源。它们影响着我们在非常广泛的日常和专业活动中的决定：例如，在哪里吃饭，住在哪里，购买哪些产品，看哪些医生，读哪些书，上哪些大学，等等。然而，在线评论的可信度和可信度岌岌可危。众所周知，大量评论是由所有者、竞争对手或由其支付费用的实体捏造的，目的是制造对产品和服务实际质量的错误看法。更重要的是，意见欺诈很普遍；虽然信用卡欺诈的比例只有0.2%或更低，但据估计，知名服务网站上20%-30%的评论可能是假的。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，观点欺诈是一种严重的法律罪行。事实上，政策制定者目前正认识到这是执法中的一个严重问题。因此，解决这一问题对企业和普通公众都非常重要。准确发现意见欺诈将使网站所有者能够提供值得信赖的内容，维护其服务的完整性，并保护在线公民免受不公平(或潜在有害的)产品和服务的影响。企业也将从具有可靠反馈的审查中受益。诚实的企业将间接获得回报，因为不道德的企业将不再容易从虚假评论中受益。因此，研究成果将对互联网商务的健康发展做出重大贡献。教育活动包括将研究成果纳入研究生水平的课程，教育公众有关欺诈行为和错误信息的知识，以及提供公开的教育材料，包括讲座和手稿。鉴于网络社区意见欺诈的关键问题，如何识别虚假评论并将罪魁祸首定为罪魁祸首？通过将PI在语言、用户行为和关系信息等各种形式的欺骗足迹方面的专业知识结合起来，该项目提出了一个研究计划，将导致对这个紧急、普遍和具有社会影响的问题提出亟需的解决方案。最终目标是通过多个信息源的协同整合来创建一个统一的检测框架；从语言学、用户行为和网络效果方面，获得全局性的信息。其主要思想是将问题描述为复合异质网络上的关系推理任务，提供了一种原则性的、可扩展的方法，可以混合和加强所有上述线索，从而有效和稳健地检测欺诈。从科学的角度来看，这项研究汇集了三个学科：自然语言分析、行为建模和图形挖掘。其结果是一套新颖的、有原则的和可扩展的技术和模型，这些技术和模型将增强我们对大规模制造和传播意见欺诈和错误信息的理解。PI将与Yelp、谷歌和亚马逊等行业合作伙伴合作，直接在网上征集虚假评论，并进行精心设计的用户研究，以测试和验证他们的技术。项目网站(http://www.cs.stonybrook.edu/~leman/PROJECTS/OPINION_FRAUD/)提供了更多信息，并将包括开放源码软件和数据集。