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Graph Anomaly Detection for Social Bot Identification: A Socio-Technical Approach to Misinformation
This project aims to detect and analyze automated bots responsible for spreading misinformation on various social media platforms by identifying abnormal patterns of behavior. By using data science and artificial intelligence methods, such as machine learning and network analysis, combined with insights from political science, the research will map how false information spreads online and malicious networks operate. This socio-technical approach will benefit Dalhousie University by bringing a unique, interdisciplinary perspective from France to the NIMS Lab’s ongoing cybersecurity and information integrity research.
Simultaneously, it will benefit CY University and CY Tech by equipping a master’s student with advanced artificial intelligence skills from a leading Canadian research institution, thereby encourage an active cross-border sharing of skills and knowledge in the critical field of digital threat detection.
Faculty Supervisor:
Nur Zincir-Heywood
Student:
Partner:
Cergy-Paris Université
Discipline:
Computer science
Sector:
Artificial Intelligence; Cyber Security; Social Innovation
University:
Dalhousie University
Program:
Globalink Research Award
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.