• DocumentCode
    1993786
  • Title

    $1.00 per RT #BostonMarathon #PrayForBoston: Analyzing fake content on Twitter

  • Author

    Gupta, Arpan ; Lamba, Hemank ; Kumaraguru, Ponnurangam

  • Author_Institution
    Indraprastha Inst. of Inf. Technol., New Delhi, India
  • fYear
    2013
  • fDate
    17-18 Sept. 2013
  • Firstpage
    1
  • Lastpage
    12
  • Abstract
    Online social media has emerged as one of the prominent channels for dissemination of information during real world events. Malicious content is posted online during events, which can result in damage, chaos and monetary losses in the real world. We analyzed one such media i.e. Twitter, for content generated during the event of Boston Marathon Blasts, that occurred on April, 15th, 2013. A lot of fake content and malicious profiles originated on Twitter network during this event. The aim of this work is to perform in-depth characterization of what factors influenced in malicious content and profiles becoming viral. Our results showed that 29% of the most viral content on Twitter, during the Boston crisis were rumors and fake content; while 51% was generic opinions and comments; and rest was true information.We found that large number of users with high social reputation and verified accounts were responsible for spreading the fake content. Next, we used regression prediction model, to verify that, overall impact of all users who propagate the fake content at a given time, can be used to estimate the growth of that content in future. Many malicious accounts were created on Twitter during the Boston event, that were later suspended by Twitter. We identified over six thousand such user profiles, we observed that the creation of such profiles surged considerably right after the blasts occurred. We identified closed community structure and star formation in the interaction network of these suspended profiles amongst themselves.
  • Keywords
    regression analysis; security of data; social networking (online); Boston crisis; Boston marathon blasts; Twitter network; community structure; fake content analysis; information dissemination; malicious content; online social media; regression prediction model; Chaos; Data collection; Earthquakes; Facebook; Feature extraction; Media; Twitter;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    eCrime Researchers Summit (eCRS), 2013
  • Conference_Location
    San Francisco, CA
  • Type

    conf

  • DOI
    10.1109/eCRS.2013.6805772
  • Filename
    6805772