$1.00 per RT #BostonMarathon #PrayForBoston: Analyzing Fake Content on Twitter
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Transcript of $1.00 per RT #BostonMarathon #PrayForBoston: Analyzing Fake Content on Twitter
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$1.00 per RT #BostonMarathon #PrayForBoston: Analyzing Fake
Content on Twitter
Sep 17, 2013
eCrime Research Summit 2013
Adi$ Gupta, Hemank Lamba,
Ponnurangam Kumaraguru (PK)
IIIT-‐Delhi, India
Unifying the Global Response to Cybercrime
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About Precog@IIITD
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Is spreading fake content an eCrime?
FAKE
RUMORS
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Boston Blasts
• Twin blasts occurred during the Boston Marathon
– April 15th, 2013 at 18:50 GMT
• 3 people were killed and 264 were injured
• Suspects Tamerlan Tsarnaev (deceased) and Dzhokhar Tsarnaev (in custody)
• Huge volume of content posted on social media websites, including TwiVer
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First Image on TwiIer (within 4 mins)
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Sample Fake Tweets
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>50,000 RTs
>30,000 RTs
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Our Contribu$ons
• We characterized the spread of fake content on TwiVer using temporal, source and user aVributes.
• We applied linear regression model to predict how viral a rumor would in future based on its current user characterisYcs.
• We analyzed the acYvity and interacYon graphs for the suspended user profiles created during Boston blasts.
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Data Descrip$on
Total tweets 7,888,374 Total users 3,677,531 Tweets with URLs 3,420,228 Tweets with Geo-tag 62,629 Retweets 4,464,201 Replies 260,627 Time of the blast Mon Apr 15 18:50 2013 Time of first tweet Mon Apr 15 18:53 2013 Time of first image Mon Apr 15 18:54 2013 Time of last tweet Thu Apr 25 01:23 2013
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Data Descrip$on
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Data Descrip$on
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Methodology
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Annota$ng Viral Tweets
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Annota$ng Viral Tweets
Six Rumors 130,690 ReTweets
128,019 Users Affected
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Temporal PaIerns
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Temporal PaIerns
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True informaYon propagaYon begins only a\er eight hours of the blast
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Predic$ng Spread of Fake Content
• Using linear regression • Predict how viral fake informaYon / rumor would be in future
based on impact of users tweeYng the rumor
• Impact based on: – Social reputaYon – Global engagement – Topical engagement
– Likability – Credibility
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Predic$ng Spread of Fake Content
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Results show it is possible to predict how viral a rumor would become in future based on aVributes of users currently propagaYng the rumor.
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Suspended Accounts
• 31,919 new TwiVer accounts created during Boston blasts, that tweeted about the event
• Out of these 19% [6,073 accounts] were deleted or suspended by TwiVer
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Fake / Malicious Accounts
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Network Analysis of Fake Accounts
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Self-‐loops
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Network Analysis of Fake Accounts
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Closed community
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Network Analysis of Fake Accounts
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Star topology
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Network Analysis of Fake Accounts
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Single links
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Conclusions & Future Work
• Based on the insights from around 10 events in last two years including: – England riots – Libya crisis – Mumbai triple blasts
– Hurricane Sandy – Boston Blasts
• We are building Real-‐Yme soluYons – Browser plug-‐in – Monitoring tools
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Media Coverage
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Ques$ons
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Thank You!�
[email protected] �[email protected] �
precog.iiitd.edu.in �
Unifying the Global Response to Cybercrime