Machine-learning algorithm develops heuristics for trustworthy tweets in time of emergency


4 Responses to “Machine-learning algorithm develops heuristics for trustworthy tweets in time of emergency”

  1. feetleet says:

    cf. ‘India thinks I’m boolean’

    Either way, it’s utterly offensive that they’re calling #kanye the minority report.

  2. semiotix says:

    Low number of happy emoticons [:-), :)] and high number of sad emoticons [:-(, :(] act as strong predictors of credibility. Some of the other important features (p-value < 0.01) were inclusion of a URL in the tweet, number of followers of the user who tweeted and presence of negative emotion words.

    Is it live now? I wonder how it’s doing with the breaking news about Seattle falling into the sea. :( If any of you have loved ones where Seattle used to be, check out for the latest news. :-( This is a sad tragic unhappy thing that makes me angry and hopeless and other negative emotions. (Please RT)

  3. bkad says:

    I still don’t “get” twitter — read: I have not figured out who to follow for twitter to be a useful source of information — but these not bad rules of thumb for assessing reliability of information generally. Credible messages cite evidence and avoid overt emotion.

    This story reminds me of a automatic youtube comment filter I read about (can’t find the link) that would suppress messages which had too much or too little punctuation, or too many capital letters, or too many emoticons, and so forth.I’m really surprised that negative emoticons are correlated with credibility — I would have guessed ANY emoticon in a communication would cut the credibility to almost nothing. But I suppose they are talking about disasters in particular, so negative emoticons would be normal (and happy ones a little suspicious). Also twitter as a medium is a little more personal than other news sources. While it would be professionally inappropriate for a journalist to react emotionally in a new article or even a blog post, we expect tweets (both from pros and citizens) to be a little more open. Or at least that’s my take.

  4. See also: 

    Predicting the Credibility of Disaster Tweets Automatically

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