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LAST year, a story about Ebola victims rising from their graves as zombies went viral. While it was obviously a hoax, it was shared millions of times. Such hoaxes happen every day. A false tweet, post or video goes viral, boosted by news websites thirsty for clicks, and not all are obviously untrue. Now tools are arriving to help us know what’s credible and what’s not.
CREDBANK, a database compiled by computer scientists at the Georgia Institute of Technology in Atlanta, is one. It couples crowdsourcing with machine learning to filter and study our social networks.
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Researchers Tanushree Mitra and Eric Gilbert started by scraping up just 1 per cent of the tweets in Twitter’s entire feed. Their software filtered and trimmed the tweets for spam before automatically sorting them into topics. The tweets were then sent to human workers on crowdsourcing site Mechanical Turk to confirm the topics and rate the messages on scales of certainty and accuracy.
The sorting takes place right after an event unfolds on Twitter, taking just a few hours. Over 96 days in 2014, the system assessed 60 million tweets about 1000 news events.
Their base finding is that roughly a quarter of tweets are not credible. Mitra and Gilbert point to the “Ebola zombie virus” hoax as a high point of rumour in their dataset.
Mitra and Gilbert have made CREDBANK publicly available so that others can hone its judgement and use it to build apps.
Google is pioneering a similar service for search results. Its Knowledge-Based Trust is a research attempt to rank pages according to the number of “true” facts they contain, rather than how many links point to them.
A system could be developed from CREDBANK that would automatically notice your uncle’s unbelievable reports of a celebrity death, says Christian Sandvig of the University of Michigan. “It would then alert you that his tweets match similar reports that were not credible. I imagine a future where bizarre stories, political hatchet-jobs, misinformation, scams, and rumours are marked with different warning shades of red.”
“I imagine a future where bizarre tweets, scams and rumours are marked with different warning signs”
This article appeared in print under the headline “Credibility filter spots hoaxes as they spread”
