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.

人们越来越多地通过在线市场直接从其他人那里购买和销售商品和服务。尽管许多在线市场允许信誉良好的买家和卖家之间进行交易，但某些平台很容易受到可疑交易的影响。该项目研究是否可以自动检测在线市场中的非法商品或服务。首先，项目团队将分析在线广告和市场政策的文本，以识别可疑活动的指标。然后，该团队将根据具体情况调整调查结果，以找到通过在线市场宣传的被盗机动车辆零件。总之，这项工作将产生识别非法在线销售信号的通用方法，可用于帮助人们选择值得信赖的市场并避免非法行为者。该项目还将为执法机构和在线市场提供见解，以收集这些市场上非法商品或服务的证据。这项研究评估了对在线消费者对消费者 (C2C) 平台中的非法活动进行建模的可行性，利用平台特征、卖家资料和广告，利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，研究团队将结合计算机科学、犯罪学和信息系统的知识来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功能、政策和服务条款。其次，基于对第一步开发的平台漏洞的理解，研究人员将生成并训练基于深度学习的语言模型来检测非法在线商务。最后，为了评估所识别标记的普遍性，调查人员将把模型应用于机动车辆零部件市场，这是一个合法的市场，有时包括提供赃物的卖家。该项目在来自不同机构和学科的不同研究人员群体与来自执法部门和行业的合作者之间建立了跨学科合作伙伴关系，以开发实用的、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价值和更广泛的影响审查标准。