Hugh Pickens writes "Will Oremus writes that 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 and in ongoing events like natural disasters, the results of Twitter misinformation can be potentially deadly. During Sandy, for instance, some tweets helped emergency responders figure out where to direct resources. Others provoked needless panic, such as one claiming that the Coney Island hospital was on fire, and a few were downright dangerous, such as the one claiming that people should stop using 911 because the lines were jammed. Now a research team at Yahoo has analyzed tweets from Chile's 2010 earthquake and looked at the potential of machine-learning algorithms to automatically assess the credibility of information tweeted during a disaster. A machine-learning classifier developed by the researchers uses 16 features to assess the credibility of newsworthy tweets and identified the features that make information more credible: credible tweets tend to be longer and include URLs; credible tweeters have higher follower counts; credible tweets are negative rather than positive in tone; and credible tweets do not include question marks, exclamation marks, or first- or third-person pronouns. Researchers at India's Institute of Information Technology also found that credible tweets are less likely to contain swear words (PDF) and significantly more likely to contain frowny emoticons than smiley faces. The bottom line is that an algorithm has the potential to work much faster than a human, and as it improves, it could evolve into an invaluable 'first opinion' for flagging news items on Twitter that might not be true writes Oremus. 'Even that wouldn't fully prevent Twitter lies from spreading or misleading people. But it might at least make their purveyors a little less comfortable and a little less smug.'"
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