Difference between revisions of "Castillo 2011"

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(Created page with '''Castillo http://www.ra.ethz.ch/cdstore/www2011/proceedings/p675.pdf == Citation == @inproceedings{conf/www/CastilloMP11, author = {Carlos Castillo and Ma…')
 
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[http://www.ra.ethz.ch/cdstore/www2011/proceedings/p675.pdf pdf link to the paper]
 
[http://www.ra.ethz.ch/cdstore/www2011/proceedings/p675.pdf pdf link to the paper]
  
=== Summary ===
+
== Summary ==
 
+
=== Data Collection===  
 +
*Automatic Event Detection (Twitter Monitor:http://www.twittermonitor.net/):
 +
We collected all the tweets matching the
 +
query during a 2-day window centered on the peak of every
 +
burst. Each of these sub-sets of tweets corresponds to what
 +
we call a topic. We collected over 2,500 such topics.
 +
*Newsworthy topic assessment (Mechanical Turk): Two type: NEWS, CHAT
 +
*Credibility assessment (Mechanical Turk): For type: i) almost certainly true, (ii) likely to be false, (iii) almost
 +
certainly false, and (iv) “I can’t decide”
  
  

Revision as of 22:31, 25 September 2012

Castillo http://www.ra.ethz.ch/cdstore/www2011/proceedings/p675.pdf


Citation

@inproceedings{conf/www/CastilloMP11,

 author    = {Carlos Castillo and
              Marcelo Mendoza and
              Barbara Poblete},
 title     = {Information credibility on twitter},
 booktitle = {WWW},
 year      = {2011},
 pages     = {675-684},
 ee        = {http://doi.acm.org/10.1145/1963405.1963500},

}


Abstract from the paper

We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors, often unintentionally. On this paper we focus on automatic methods for assessing the credibility of a given set of tweets. Specifically, we analyze microblog postings related to “trending” topics, and classify them as credible or not credible, based on features extracted from them. We use features from users’ posting and re-posting (“re-tweeting”) behavior, from the text of the posts, and from citations to external sources. We evaluate our methods using a significant number of human assessments about the credibility of items on a recent sample of Twitter postings. Our results shows that there are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70% to 80%.

Online version

pdf link to the paper

Summary

Data Collection

We collected all the tweets matching the query during a 2-day window centered on the peak of every burst. Each of these sub-sets of tweets corresponds to what we call a topic. We collected over 2,500 such topics.

  • Newsworthy topic assessment (Mechanical Turk): Two type: NEWS, CHAT
  • Credibility assessment (Mechanical Turk): For type: i) almost certainly true, (ii) likely to be false, (iii) almost

certainly false, and (iv) “I can’t decide”


Background

What's the interesting in this paper

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