CAREER: Tracking, Revealing and Detecting Crowdsourced Manipulation
National Science Foundation Award Number CNS 1755536 PI: Kyumin Lee Duration (expected): March 2016 - February 2021Project 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
- N. Vo, K. Lee, and T. Tran. MRAttractor: Detecting Communities from Large-Scale Graphs. Big Data, December 2017.
- T. Tran, K. Lee, N. Vo, and H. Choi. Identifying On-time Reward Delivery Projects with Estimating Delivery Duration on Kickstarter. ASONAM, July 2017.
- N. Vo, K. Lee, C. Cao, T. Tran, and H. Choi. Revealing and Detecting Malicious Retweeter Groups. ASONAM, July 2017.
- Y. Liao, T. Tran, D. Lee, and K. Lee. Understanding Temporal Backing Patterns in Online Crowdfunding Communities. WebSci, June 2017.
- T. Tran, and K. Lee. Characteristics of On-time and Late Reward Delivery Projects. ICWSM, May 2017.
- P. Badri, K. Lee, D. Lee, T. Tran, and J. Zhang. Uncovering Fake Likers in Online Social Networks. CIKM, October 2016.
- H. Choi, K. Lee, and S. Webb. Detecting Malicious Campaigns in Crowdsourcing Platforms. ASONAM, August 2016.
- T. Tran, and K. Lee. Understanding Citizen Reactions and Ebola-Related Information Propagation on Social Media. ASONAM, August 2016.
Participants
- Kyumin Lee, PI
- Nguyen Vo, PhD Student
- Thanh Tran, PhD Student
Project Alumni
- Hongkyu Choi (MS, 2017)
- Prudhvi Badri (MS, 2016)