CAREER: Tracking, Revealing and Detecting Crowdsourced Manipulation

该项目的目标是创建算法，框架和系统，以保护开放的Web生态系统免受新兴威胁的影响。该项目旨在（i）分析恶意任务和众转者的行为;（ii）通过开发新型恶意任务检测器来检测众包平台上的恶意任务;（iii）设计和构建任务黑名单;（iv）揭示众转者的生态系统并检测众转者;（v）将众转者检测方法与其他恶意参与者检测方法相结合。众包系统成功地利用了数百万“众包”工人的注意力来解决棘手的问题。从用于危机绘图、蛋白质折叠、翻译的专业系统到通用众包平台。然而，这些积极的机会也有邪恶的对应物：大规模的“众筹”，其中大量的廉价工人可以组织起来在社交媒体上传播恶意URL，形成人工草根运动（“astroturf”），以及操纵搜索引擎。因此，众包操纵威胁到开放网络生态系统的基础，降低在线社交媒体的质量，降低我们对搜索引擎的信任，操纵政治舆论，最终降低网络空间的安全性和可信度。研究成果将供公众使用。该项目的教育和推广工作通过课程编制、讲习班、对代表人数不足的妇女的直接培训以及工业界的参与，与研究目标密切相关。该项目的智力价值在于，它将推进当前的安全系统，以对抗网络空间中的众包操纵。该项目通过检测众包平台中的恶意任务，从根本上改变了恶意任务问题的格局。恶意任务的早期检测有可能将我们的解决方案转变为安全和值得信赖的信息系统。鉴于我们的恶意任务检测系统，识别出的恶意任务可以用作创建新黑名单的样本。黑名单有可能防止恶意任务传播到流行的在线目标站点。鉴于我们的新技术，在不久的将来，检测几乎所有的crowdturfers成为一种明显的可能性。总的来说，该项目将促进知识和理解众包操纵问题，拟议的检测框架将补充目前的安全系统，防止众包操纵。拟议工作的更广泛影响包括在促进教学、培训和学习的同时促进发现和理解。为了造福社会，所提出的恶意任务和众转检测框架包括任务黑名单将使众包服务提供商和目标网站提供商能够检测众包操纵，同时保护信息质量和信任。

The goal of this project is to create the algorithms, frameworks, and systems for defending the open web ecosystem from emerging threats. This project aims to (i) analyze malicious tasks and behaviors of crowdturfers; (ii) detect malicious tasks on crowdsourcing platforms by developing novel malicious task detectors; (iii) design and build a task blacklist; (iv) uncover the ecosystem of crowdturfers and detect crowdturfers; (v) combine crowdturfer detection approaches with other malicious participants detection approaches. Crowdsourcing systems have successfully leveraged the attention of millions of "crowdsourced" workers to tackle vexing problems. From specialized systems for crisis mapping, for protein folding, for translation to general-purpose crowdsourcing platforms. However, these positive opportunities have sinister counterparts: large-scale "crowdturfing", wherein masses of cheaply paid workers can be organized to spread malicious URLs in social media, formation of artificial grassroots campaigns ("astroturf"), and manipulation of search engines. As a result, crowdsourced manipulation threatens the foundations of the open web ecosystem, reducing the quality of online social media, degrading our trust in search engines, manipulating political opinion and ultimately, reducing security and trustworthiness of cyberspace. Products of the research will be available for public use. The education and outreach efforts of the project are tightly linked to the research goals through curriculum development, workshops, direct training of underrepresented women, and involvement of industry. The intellectual merit of the project is it will advance the current security systems against crowdsourced manipulation in cyberspace. This project fundamentally alters the landscape of malicious task problems by detecting malicious tasks in crowdsourcing platforms. Early detection of malicious tasks has the potential to transform our solutions for secure and trustworthy information systems. Given our malicious task detection systems, identified malicious tasks can be used as samples for creating new blacklists. The blacklists have the potential to prevent propagation of malicious tasks to popular online target sites. Given our novel techniques, detecting almost all crowdturfers becomes a distinct possibility in the near future. Overall, this project will advance knowledge and understanding the crowdsourced manipulation problem, and the proposed detection framework will complement the current security systems against crowdsourced manipulation. The broader impacts of the proposed work include advances to discovery and understanding while promoting teaching, training and learning. To benefit society, the proposed malicious task and crowdturfer detection framework including task blacklists will enable crowdsourcing service providers and target sites providers to detect crowdsourced manipulation with protecting information quality and trust.