CAREER: Tracking, Revealing and Detecting Crowdsourced Manipulation

National Science Foundation Award Number CNS 1755536
PI: Kyumin Lee
Duration (expected): March 2016 - February 2021

Project Overview

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.

Publications

Participants

  • Kyumin Lee, PI
  • Nguyen Vo, PhD Student
  • Thanh Tran, PhD Student

Project Alumni

  • Hongkyu Choi (MS, 2017)
  • Prudhvi Badri (MS, 2016)