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Serious concerns have been raised about the role of 'socialbots' inmanipulating public opinion and influencing the outcome of elections byretweeting partisan content to increase its reach. Here we analyze the role andinfluence of socialbots on Twitter by determining how they contribute toretweet diffusions. We collect a large dataset of tweets during the 1st U.S.Presidential Debate in 2016 (#DebateNight) and we analyze its 1.5 million usersfrom three perspectives: user influence, political behavior (partisanship andengagement) and botness. First, we define a measure of user influence based onthe user's active contributions to information diffusions, i.e. their tweetsand retweets. Given that Twitter does not expose the retweet structure - itassociates all retweets with the original tweet - we model the latent diffusionstructure using only tweet time and user features, and we implement a scalablenovel approach to estimate influence over all possible unfoldings. Next, we usepartisan hashtag analysis to quantify user political polarization andengagement. Finally, we use the BotOrNot API to measure user botness (thelikelihood of being a bot). We build a two-dimensional "polarization map" thatallows for a nuanced analysis of the interplay between botness, partisanshipand influence. We find that not only social bots are more active on Twitter -starting more retweet cascades and retweeting more -- but they are 2.5 timesmore influential than humans, and more politically engaged. Moreover,pro-Republican bots are both more influential and more politically engaged thantheir pro-Democrat counterparts. However we caution against blanket statementsthat software designed to appear human dominates political debates. We findthat many highly influential Twitter users are in fact pro-Democrat and thatmost pro-Republican users are mid-influential and likely to be human (lowbotness).

#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate