Broker Bots: Analyzing automated activity during High Impact Events on Twitter

Twitter is now an established and a widely popular news medium. Be it normal banter or a discussion on high impact events like Boston marathon blasts, February 2014 US Icestorm, etc., people use Twitter to get updates and also broadcast their thoughts and views. Twitter bots have today become very common and acceptable. People are using them to get updates about emergencies like natural disasters, terrorist strikes, etc., users also use them for getting updates about different places and events, both local and global. Twitter bots provide these users a means to perform certain tasks on Twitter that are both simple and structurally repetitive, at a much higher rate than what would be possible for a human alone. During high impact events these Twitter bots tend to provide a time critical and a comprehensive information source with information aggregated form various different sources. In this study, we present how bots participate in discussions and augment them during high impact events. We identify bots in 5 high impact events for 2013: Boston blasts, February 2014 US Icestorm, Washington Navy Yard Shooting, Oklahoma tornado, and Cyclone Phailin. We identify bots among top tweeters by getting all such accounts manually annotated. We then study their activity and present many important insights. We determine the impact bots have on information diffusion during these events and how they tend to aggregate and broker information from various sources to different users. We also analyzed their tweets, list down important differentiating features between bots and non bots (normal or human accounts) during high impact events. We also show how bots are slowly moving away from traditional API based posts towards web automation platforms like IFTTT, dlvr.it, etc. Using standard machine learning, we proposed a methodology to identify bots/non bots in real time during high impact events. This study also looks into how the bot scenario has changed by comparing data from high impact events from 2013 with data from similar type of events from 2011. Bots active in high impact events generally don’t spread malicious content. Lastly, we also go through an in-depth analysis of Twitter bots who were active during 2013 Boston Marathon Blast. We show how bots because of they’re programming structure don’t pick up rumors easily during these events and even if they do; they do it after a long time.