Castillo 2011

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

• Automatic Event Detection (Twitter Monitor:http://www.twittermonitor.net/): 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 a topic. Over 2,500 such topics are collected.
• Newsworthy topic assessment (Mechanical Turk): Two type: NEWS, CHAT
• Credibility assessment (Mechanical Turk): Four type: i) almost certainly true, (ii) likely to be false, (iii) almost certainly false, and (iv) “I can’t decide”

Automatic Credibility Analysis

Four types of features depending on their scope: message-based features, user-based features, topic-based features, and propagation- based features.

• Message-based features consider characteristics of messages,

these features can be Twitter-independent or Twitterdependent. Twitter-independent features include: the length of a message, whether or not the text contains exclamation or question marks and the number of positive/negative sentiment words in a message. Twitter-dependent features include features such as: if the tweet contains a hashtag, and if the message is a re-tweet.

• User-based features consider characteristics of the users

which post messages, such as: registration age, number of followers, number of followees (“friends” in Twitter), and the number of tweets the user has authored in the past.

• Topic-based features are aggregates computed from the

previous two feature sets; for example, the fraction of tweets that contain URLs, the fraction of tweets with hashtags and the fraction of sentiment positive and negative in a set.

• Propagation-based features consider characteristics related

to the propagation tree that can be built from the retweets of a message. These includes features such as the depth of the re-tweet tree, or the number of initial tweets of a topic.

Automatic Assessing Credibility

Standard machine learning techniques, the best they report is using J48 decision tree.

Results:

Results for the credibility classification.

Class TP_Rate FP_Rate Prec. Recall F1

A (“true”) 0.825 0.108 0.874 0.825 0.849

B (“false”) 0.892 0.175 0.849 0.892 0.87

W. Avg. 0.860 0.143 0.861 0.860 0.86

Feature Level Analysis

Top feature that contribute more on deciding credibility:

• Tweets having an URL is the root of the tree.
• Sentiment-based feature like fraction of negative sentiment
• Low credibility news are mostly propagated by users who have not written many message in the past

Interesting Aspect

I like the coding scheme of this paper. It is reasonable and comprehensive. Some of the conclusion that drew from the paper is interesting to look at. For example

• Among several other features, newsworthy topics tend to include URLs and to have deep propagation trees
• Among several other features, credible news are propagated through authors that have previously written a large number of messages, originate

at a single or a few users in the network, and have many re-posts.

Related Papers

• T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes Twitter users: real-time event detection by social sensors.

In Proceedings of the 19th international conference on World wide web, WWW ’10, pages 851–860, New York, NY, USA, April 2010. ACM

• J. Sankaranarayanan, H. Samet, B. E. Teitler, M. D.Lieberman, and J. Sperling. TwitterStand: news in tweets. In GIS ’09: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 42–51, New York, NY, USA, November 2009. ACM Press.

Study Plan

Papers you may want to read to understand this paper.