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$1.00 per RT #BostonMarathon #PrayForBoston: Analyzing Fake Content on Twitter
 

$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 $1.00 per RT #BostonMarathon #PrayForBoston: Analyzing Fake Content on Twitter Presentation Transcript

    • $1.00 per RT #BostonMarathon #PrayForBoston: Analyzing Fake Content on Twitter Sep  17,  2013   eCrime  Research  Summit  2013   Adi$  Gupta,  Hemank  Lamba,     Ponnurangam  Kumaraguru  (PK)   IIIT-­‐Delhi,  India   Unifying the Global Response to Cybercrime
    • 2   About  Precog@IIITD  
    • Is  spreading  fake  content  an  eCrime?   FAKE   RUMORS   3   $  
    • 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   4  
    • First  Image  on  TwiIer  (within  4  mins)   5  
    • Sample  Fake  Tweets   6   >50,000  RTs   >30,000  RTs  
    • Our  Contribu$ons   •  We  characterized  the  spread  of  fake  content  on  TwiVer  using   temporal,  source  and  user  aVributes.   •  We  applied  linear  regression  model  to  predict  how  viral  a   rumor  would  in  future  based  on  its  current  user   characterisYcs.   •  We  analyzed  the  acYvity  and  interacYon  graphs  for  the   suspended  user  profiles  created  during  Boston  blasts.     7  
    • Data  Descrip$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 8  
    • Data  Descrip$on   9  
    • Data  Descrip$on   10  
    • Methodology   11  
    • Annota$ng  Viral  Tweets  
    • Annota$ng  Viral  Tweets   Six  Rumors   130,690  ReTweets   128,019  Users  Affected  
    • Temporal  PaIerns   14  
    • Temporal  PaIerns   15   True  informaYon   propagaYon  begins  only   aer  eight  hours  of  the   blast  
    • Predic$ng  Spread  of  Fake  Content   •  Using  linear  regression   •  Predict  how  viral  fake  informaYon  /  rumor  would  be  in  future   based  on  impact  of  users  tweeYng  the  rumor   •  Impact  based  on:   –  Social  reputaYon   –  Global  engagement   –  Topical  engagement   –  Likability   –  Credibility   16  
    • Predic$ng  Spread  of  Fake  Content   17   Results  show  it  is  possible  to  predict  how  viral  a  rumor  would  become  in   future  based  on  aVributes  of  users  currently  propagaYng  the  rumor.  
    • 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   18  
    • Fake  /  Malicious  Accounts   19  
    • Network  Analysis  of  Fake  Accounts   20   Self-­‐loops  
    • Network  Analysis  of  Fake  Accounts   21   Closed  community  
    • Network  Analysis  of  Fake  Accounts   22   Star  topology  
    • Network  Analysis  of  Fake  Accounts   23   Single  links  
    • 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   24  
    • Media  Coverage   25  
    • Ques$ons   ? 26  
    • 27   Thank You! aditig@iiitd.ac.in pk@iiitd.ac.in precog.iiitd.edu.in Unifying the Global Response to Cybercrime
    • For any further information, please write to   pk@iiitd.ac.in   precog.iiitd.edu.in