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. 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.
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