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

机译:每个RT $ 1.00 #BostonMarathon #PrayForBoston:在Twitter上分析假内容

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摘要

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.
机译:在线社交媒体已成为现实世界事件中信息传播的重要渠道之一。事件期间,恶意内容会在线发布,这可能导致现实世界中的破坏,混乱和金钱损失。我们分析了2013年4月15日发生的波士顿马拉松爆炸事件期间产生的内容,即Twitter等此类媒体。在此事件中,Twitter网络上产生了许多虚假内容和恶意个人资料。这项工作的目的是对影响恶意内容和配置文件成为病毒的因素进行深入的表征。我们的结果表明,在波士顿危机期间,Twitter上最具病毒性的内容中有29%是谣言和虚假内容; 51%是一般性意见和评论;剩下的就是真实的信息。我们发现,拥有较高社会声誉和经过验证的帐户的大量用户是散布虚假内容的原因。接下来,我们使用回归预测模型来验证在给定时间传播假内容的所有用户的总体影响,可以用来估计该内容在将来的增长。在波士顿事件期间,Twitter上创建了许多恶意帐户,后来这些帐户被Twitter暂停。我们发现了六千多个此类用户档案,我们观察到爆炸发生后,此类档案的创建激增。我们在这些悬浮轮廓之间的相互作用网络中确定了封闭的社区结构和恒星形成。

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