This course aims to challenge assumptions in the fight against disinformation by questioning the scalability of trustworthiness in information. It covers the use of machine learning to combat disinformation, emphasizing the importance of understanding human qualities in spreading information. The course teaches about team science, credibility coalition, fact-checking, and the contextual nature of trust. The teaching method includes lectures on findings from science and technology studies, neurocognitive development, and new literacies research. This course is intended for individuals interested in combating disinformation and understanding the complexities of trust in information dissemination.
Overview
Syllabus
Introduction
Two powerful forces
The Internet
Team Science
Credibility Coalition
Solving the Problem
Facts are Social
Fact
Evidence
Arguments
Fact checking
Yellow journalism
Professionalization
Trust is contextual
So little trust in medical facts
Team loyalties
Team scriptural inference
Team 5G
Team Q
Team Q Anon
John Huber
Five Eyes
Priori assumptions
Human understanding of trust
Its a social problem
Googles page rank
Team social media
ivory tower Jerk
Local institutions
Fact checkers
Content moderation
Algorithms should not promote strong emotions
Did we miss the boat on saturation news
Fight misinformation on a human scale
Be a calming presence
Taught by
USENIX Enigma Conference
