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

This study found that 29% of the most viral content on Twitter during the Boston bombing crisis were rumors and fake content.AbstractOnline 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

Emergent System using Tweet Analyzer Naturally Inspired Computing Approach

Nowadays much system has developed to reach the people during disasters. A social interaction with the micro blogging services has vastly increased. Twitter a well popularsocial mediumhas scarcity of attention makes people interacting with each other.Thispaper explores the use of twitter for disaster event using text classifier.It analyses the targeted event with tweeted text and identifies the target and its location. By the use of SVMs, thetext classifiers are achieved.SVM performs the methods of Bayesian filtering for the informative messages of particular event. This methodof filtering works best compare with the other methods for estimating the informative messages. As an application we are sending an E-mail and SMS alert message through the twitter and thedeclared set of friends and followers follows the messages