EAGER: DCL: SaTC: Enabling Interdisciplinary Collaboration: Using NLP to Identify Suspicious Transactions in Omnichannel Online C2C Marketplaces

EAGER：DCL：SaTC：实现跨学科协作：使用 NLP 识别全渠道在线 C2C 市场中的可疑交易

• 批准号: 2210091
• 负责人: Pablo Rivas
• 金额: $ 29.83万
• 依托单位: Baylor University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210091&HistoricalAwards=false
• 关键词: EAGER; DCL; SaTC; Enabling; Interdisciplinary

Increasingly, people buy and sell goods and services directly from other people via online marketplaces. While many online marketplaces enable transactions among reputable buyers and sellers, some platforms are vulnerable to suspicious transactions. This project investigates whether it is possible to automate the detection of illegal goods or services within online marketplaces. First, the project team will analyze the text of online advertisements and marketplace policies to identify indicators of suspicious activity. Then, the team will adapt the findings to a specific context to locate stolen motor vehicle parts advertised via online marketplaces. Together, the work will lead to general ways to identify signals of illegal online sales that can be used to help people choose trustworthy marketplaces and avoid illicit actors. This project will also provide law enforcement agencies and online marketplaces with insights to gather evidence on illicit goods or services on those marketplaces. This research assesses the feasibility of modeling illegal activity in online consumer-to-consumer (C2C) platforms, using platform characteristics, seller profiles, and advertisements to prioritize investigations using actionable intelligence extracted from open-source information. The project is organized around three main steps. First, the research team will combine knowledge from computer science, criminology, and information systems to analyze online marketplace technology platform policies and identify platform features, policies, and terms of service that make platforms more vulnerable to criminal activity. Second, building on the understanding of platform vulnerabilities developed in the first step, the researchers will generate and train deep learning-based language models to detect illicit online commerce. Finally, to assess the generalizability of the identified markers, the investigators will apply the models to markets for motor vehicle parts, a licit marketplace that sometimes includes sellers offering stolen goods. This project establishes a cross-disciplinary partnership among a diverse group of researchers from different institutions and academic disciplines with collaborators from law enforcement and industry to develop practical, actionable insights.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

越来越多的人通过在线市场直接从其他人那里购买和销售商品和服务。虽然许多在线市场允许信誉良好的买家和卖家之间进行交易，但一些平台很容易受到可疑交易的影响。这个项目调查是否有可能自动检测在线市场内的非法商品或服务。首先，项目组将分析在线广告和市场政策的文本，以确定可疑活动的指标。然后，该团队将根据具体情况调整发现，以定位通过在线市场广告的被盗汽车零部件。总而言之，这项工作将带来识别非法在线销售信号的一般方法，这些信号可以用来帮助人们选择值得信赖的市场，避免非法行为者。该项目还将为执法机构和在线市场提供洞察力，以收集这些市场上非法商品或服务的证据。这项研究评估了在线消费者对消费者(C2C)平台中非法活动建模的可行性，使用平台特征、卖家档案和广告来使用从开源信息中提取的可操作情报来确定调查的优先顺序。该项目围绕三个主要步骤进行组织。首先，研究小组将结合计算机科学、犯罪学和信息系统的知识，分析在线市场技术平台政策，并确定使平台更容易受到犯罪活动影响的平台特征、政策和服务条款。其次，在了解第一步开发的平台漏洞的基础上，研究人员将生成并训练基于深度学习的语言模型，以检测非法在线交易。最后，为了评估识别出的标记的普适性，调查人员将把这些模型应用于汽车零部件市场，这是一个合法的市场，有时会包括提供赃物的卖家。该项目在来自不同机构和学术学科的不同研究人员之间建立了跨学科的合作伙伴关系，与执法部门和行业的合作者一起开发实用的、可操作的见解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。