Social media hoaxes: Could machine learning debunk false Twitter rumors before they spread?
By Will Oremus, Slate Magazine, Dec 14, 2002
When something momentous is unfolding—the Arab Spring, Hurricane Sandy, Friday’s horrific elementary school shooting in Connecticut—Twitter is the world’s fastest, most comprehensive, and least reliable source of breaking news. If you were on the microblogging site Friday afternoon, you were among the first to hear the death toll, watch the devastated reactions, and delve into the personal details of the man the media initially identified as a killer. But there’s also a good chance you were taken in by some of the many falsehoods that were flying, like a letter one of the young victims purportedly wrote to his mother before the shooter entered the classroom. And, of course, all of those social media pages that were making the rounds turned out to belong to innocent people, including the real suspect’s brother.
For full text of the article, visit Social media hoaxes: Could machine learning debunk false Twitter rumors before they spread? – Slate Magazine.
- Building a Better Truth Machine (slate.com)
- Predicting the Credibility of Disaster Tweets Automatically (irevolution.net)
- Social Media In a Disaster: From Hoaxes to Healing (blogs.sap.com)
Dr. Lea Shanley is the founder and former co-Chair of the Federal Community of Practice on Crowdsourcing and Citizen Science, a vibrant community of 200 federal employees from more than 35 agencies. She is also a co-founding member of the Citizen Science Association. Dr. Shanley recently served as a Presidential Innovation Fellow at NASA, where she helped to foster a culture of open innovation. Prior to this, she founded and directed the Commons Lab at the Wilson Center, served in the US Senate as a Congressional Science Fellow, and worked with local and tribal communities to develop GIS-based decision support systems for city planning, natural resource management, coastal management, and disaster response through the University of Wisconsin-Madison.
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