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Researchers Develop an Internet Truth Machine
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.'"
This is really interesting research, but it's also based on one event in one country.
Conclusions based on what may be language or cultural norms (such as "did you phrase in the positive or the negative") might not translate to other locales well (e.g. Hurricane Sandy in the US).
But, then, that's what's great about science. Testable predictions we can apply to data.
How effective would this be on real media? I bet it'd put those bastards in their place! :)
So it provides a first opinion on first posts, sort of. Neat, but I do wonder how accurate this is going to be to vet individual tweets. Twitter trolls may get wise to this and game the system to get their stuff past this filter. A bit like phishers learning how to spell. In the end, the best check is still independent verification, for example by other people tweeting the same thing (not just retweeting of course). If this system could automatically group and cross-verify tweets from multiple sources on the same subject, that would be a step in the right direction.
If construction was anything like programming, an incorrectly fitted lock would bring down the entire building...
Couldn't some enterprising douche programmer use simular programs to write better misleading tweets.
It's interesting to note, that a seismology student at a university in Chile finally had enough nonsense from false information over Twitter, etc about earthquakes, that he directly wired a big batch of seismographs to directly post their results via Twitter. The last I knew, they had over 1 million followers, and this particular student has been getting big thank yous from residents of the country.
@Mindless Drivel: 100% of Twitter posts ever Tweeted.
Twitter is the world's fastest, most comprehensive, and least reliable source of breaking news
Twitter has dethroned Fox News?!?
I trust you, Anonymous Coward.
Filthy, filthy copyrapists!
One of the criteria in their algorithm seems to be that credible tweets were
They were evaluating tweets about a disaster; not a lot of smiley faces there.
The algorithm seems to have a bias toward bad news. So, if my buddy tweets that a rare Belgian beer will be available at the local liquor store, the algorithm will decide that it isn't credible because of the smiley face.
We just had the above case. Beer that you usually have to cross the Atlantic to get became available for about 30 minutes locally. Some of us lined up starting at 3:00 AM. I would have been really ticked off if some algorithm had made me miss the news.
There's two topics here, one is use of potentially valuable information by, say, emergency responders (leads, evidence, etc.). The program could be useful. The second (e.g. "don't use 911") is "a headline", i.e. it is aimed at spreading news (or troll farts) as media to the social public. These are definitely two completely separate problems to solve. The second problem is best solved by evolution, as people who get their "news" off of social media become even stupider than they were to begin with and die off.
Gently reply
Reality is the stuff that doesn't go away when you stop believing it.
Don't be a pedantic asshole. We can't determine the absolute truth, but we can get a close enough approximation.
It is Twitter, not Tweeter. Therefore Twits. Not Tweets. Twits.
Of course, in just the same way that spammers can game Bayesian spam filters or rule-matching pattern filters by knowing what the rules are, given a known set of rules that attempt to assess credibility of tweet allows someone to tweak their tweets in order to be assessed as having high credibility: ;>(] :>( beebs
:>( -- beebs
/.'s /-code and is not part of the wc wordcount :>(
1 -- max out your tweet length
2 -- include an URL [doesn't say whether to use a link shrtnr
3 -- use a Twitter account with a high number of followers
4 -- use a negative tone
5 -- no question marks or exclamation points
6 -- use 2nd person (same as don't use 1st or 3rd person)
7 -- don't use swear words
8 -- use a sad emoticon
.
Example to maximize this:
a - break into / hack a high follower account (e.g. justinbieber) and tweet: cat > finaltweet
You should know Mayan Calendar sez: world ending this week. Confirmed@ http://netcraft.calendar.mayan/ you go hug loved 1s now.
wc finaltweet
1 20 139 finaltweet
First iteration was:
gia@sodium$ cat > count2
You should know that Mayan Calendar says : world ending within week. Confirmed by http://netcraft.calendar.mayan/ , you should hug loved ones now.
gia@sodium$ wc count2
1 25 159 count2
Please note that the "[netcraft.calendar.mayan]" was inserted by
If you weren't aware that Hurricane Sandy, Irene or whatever occurred, just tune into the local television and watch the car commercials. If I see one more Maxon, Salerno Dwayne, Rutherford Ford or Honda Hurricane Sandy stimulus event, I'm going to throw up. THAT is how you know something bad has happened.
The basic problem with any such approach is that tweets are individual opinions and you cannot arrive at the truth or falsehood of objective facts by analyzing a collection of he-saids and she-saids.
The hospital is either on fire or it is not on fire, regardless of what anybody says.
Wrong. Einstein's theory of relativity doesn't say that reality is relative. Indeed it is very absolute in that theory. What is relative is the way we slice it into space and time.
The Tao of math: The numbers you can count are not the real numbers.
Who stops to type emoticons in the middle of a natural disaster (including switching to the alternate keyboard to get those characters)?
It happens. When the Rabaul Queen capsized[*] in heavy seas, killing an estimated 321 people, there were dozens of tweets and facebook posts from people on board. They used emoticons because it's a lot easier to write :-( than it is to write 'I'm really frightened right now.' Let me tell you, when I was assigned to write about the disaster, it was very, very difficult to read those posts and remain unmoved.
Moral: Don't make assumptions about people's state of mind unless you have some insight into what they're experiencing.
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[*] Of course, you've never heard of it, because nobody important was killed, just a bunch of dark people from nowhere important. Not a First World Problem....
Crumb's Corollary: Never bring a knife to a bun fight.