ANU algorithms define bot influence in 2016 US Election
New algorithms developed at the Australian National University (ANU) have found bots on Twitter were around two-and-a-half times more influential than humans during the first US Presidential debate in 2016. The researchers from the ANU Research School of Computer Science and School of Sociology also discovered there were far fewer bots than previously thought, but they had a disproportionate level of influence relative to their number. The team analysed 6.4 million tweets generated over the 90 minutes before, during and after the first televised Presidential debate between Hilary Clinton and Donald Trump on September 16, 2016. They found bots were more politically engaged than humans and more likely to be pro-republican. Significantly, the analysis showed 'highly influential' human users were more likely to be more pro-Democrat, such as Oprah Winfrey, but a small percentage of mostly pro-Republican bots were more successful in influencing the Twitter information space during the debate. The team co-led by Dr Timothy Graham and Dr Marian-Andrei Rizoiu spent months collecting data to determine if the 1.5 million user accounts active during the debate were actually humans or not. They classified four categories: Bots (and bot-like), Humans, Deleted Accounts (more likely to be bots) and Protected Accounts (more likely to be humans).
