Download - Faking Sandy: Characterizing and Identifying Fake Images on Twitter during Hurricane Sandy
Faking Sandy: Characterizing and Identifying Fake Images on Twitter
during Hurricane Sandy
Adi$ Gupta Hemank Lamba
Ponnurangam Kumaraguru Anupam Joshi
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Agenda
Background Mo$va$on Main contribu$ons Data descrip$on Methodology Results Future work
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Background: Hurricane Sandy
Dates: Oct 22-‐ 31, 2012 Category 3 storm Damages worth $75 billion Coast of NE America
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Motivation
FAKE IMAGES
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Motivation
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Our Contributions
Performed in-‐depth characteriza$on of tweets sharing fake images on TwiVer during Hurricane Sandy - Tweets containing the fake images URLs were mostly retweets (86%)
- Just 11% overlap between the retweet and follower graphs of tweets containing fake images.
Applied classifica$on algorithms to dis$nguish between tweets containing fake and real images. - Best accuracy of 97% was achieved using decision tree classifier, using tweet based features.
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Methodology
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Data Collec$on and Filtering
Data Characteriza$on
Feature Genera$on
Obtaining Ground Truth
Evalua$ng Results
Classifica$on Module
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Data Description
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Total tweets 1,782,526 Total unique users 1,174,266 Tweets with URLs 622,860
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Data Filtering
Reputable online resource to filter fake and real images - Guardian collected and publically distributed a list of fake and true images shared during Hurricane Sandy
One of the biggest fake content propaga$on datasets that have been studied by researchers
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Tweets with fake images 10,350
Users with fake images 10,215
Tweets with real images 5,767
Users with real images 5,678
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Characterization – Fake Image Propagation
86% of tweets spreading the fake images were retweets
Top 30 users out of 10,215 users (0.3%) resulted in 90% of the retweets of fake images
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Network Analysis
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Tweet – Retweet graph for the spread of fake images at ‘nth’ and ‘n+1th‘ hour
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Role of Explicit Twitter Network
Analyzing role of follower network in fake image propaga$on
We crawled the TwiVer network for all users who tweeted the fake image URLs
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Algorithm
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Results
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Total edges in the retweet network 10,508
Total edges in the follower-‐followee network 10,799,122
Total edges that exist in both retweet network and the follower-‐ followee network
1,215
%age Overlap 11%
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Classification
5 fold cross valida$on
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Tweet Features [F2] Length of Tweet Number of Words
Contains Ques$on Mark? Contains Exclama$on Mark? Number of Ques$on Marks
Number of Exclama$on Marks Contains Happy Emo$con Contains Sad Emo$con
Contains First Order Pronoun Contains Second Order Pronoun Contains Third Order Pronoun
Number of uppercase characters Number of nega$ve sen$ment words Number of posi$ve sen$ment words
Number of men$ons Number of hashtags Number of URLs Retweet count
User Features [F1] Number of Friends Number of Followers Follower-‐Friend Ra$o
Number of $mes listed User has a URL
User is a verified user Age of user account
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Classification Results
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F1 (user) F2 (tweet) F1+F2
Naïve Bayes 56.32% 91.97% 91.52%
Decision Tree 53.24% 97.65% 96.65%
• Best results were obtained from Decision Tree classifier, we got 97% accuracy in predic$ng fake images from real.
• Tweet based features are very effec$ve in dis$nguishing fake images tweets from real, while the performance of user based features was very poor.
• Our results provided a proof of concept that, automated techniques can be
used in iden$fying real images from fake images posted on TwiVer.
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Future Work
Fake Charity
Rumor
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Some Attractions
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Some Attractions
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Q & A
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