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What and Who
Title:#DebateNight :The Role and Influence of Socialbots on Twitter During the 1st U.S. Presidential Debate
Speaker:Marian-Andrei Rizoiu
coming from:Australian National University
Speakers Bio: Dr. Marian-Andrei Rizoiu is a Research Fellow with the Australian National University, studying the dynamics of human attention in the online environment. His research has made several key contributions, particularly to the areas of online popularity prediction and online privacy. For the past four years, he has been developing theoretical models for online information diffusion, which can account for complex social phenomena, such as the rise and fall of online popularity, the spread of misinformation or the adoption of disruptive technologies. He approached questions such as "Why did X become popular, but not Y?" and "How can items be promoted?" with implications in advertising and marketing. Marian-Andrei has also worked on detecting the evolution of privacy loss over time. His research has shown that privacy "leaks" over time and it identified the factors causing the loss: the individual's own actions and the environment. The conclusions were staggering: privacy continues to decrease even for users who retired from activity. Marian-Andrei published in the most selective venues of the field (such as WWW, WSDM, ICWSM or CIKM) and his work has received significant media attention, including from the Wikimedia Foundation for the work concerning the privacy of Wikipedia editors (which featured in the March 2016 Wikimedia Research Showcase). See more at
Event Type:SWS Colloquium
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
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Level:AG Audience
Date, Time and Location
Date:Thursday, 3 May 2018
Duration:60 Minutes
Serious concerns have been raised about the role of `socialbots' in manipulating public opinion and influencing the outcome of elections by retweeting partisan content to increase its reach. Here we analyze the role and influence of socialbots on Twitter by determining how they contribute to retweet diffusions. We collect a large dataset of tweets during the 1st U.S. presidential debate in 2016 and we analyze its 1.5 million users from three perspectives: user influence, political behavior (partisanship and engagement) and botness. First, we define a measure of user influence based on the user's active contributions to information diffusions, i.e. their tweets and retweets. Given that Twitter does not expose the retweet structure -- it associates all retweets with the original tweet -- we model the latent diffusion structure using only tweet time and user features, and we implement a scalable novel approach to estimate influence over all possible unfoldings. Next, we use partisan hashtag analysis to quantify user political polarization and engagement. Finally, we use the BotOrNot API to measure user botness (the likelihood of being a bot). We build a two-dimensional "polarization map" that allows for a nuanced analysis of the interplay between botness, partisanship and influence. We find that not only are socialbots more active on Twitter -- starting more retweet cascades and retweeting more -- but they are 2.5 times more influential than humans, and more politically engaged. Moreover, pro-Republican bots are both more influential and more politically engaged than their pro-Democrat counterparts. However we caution against blanket statements that software designed to appear human dominates politics-related activity on Twitter. Firstly, it is known that accounts controlled by teams of humans (e.g. organizational accounts) are often identified as bots. Secondly, we find that many highly influential Twitter users are in fact pro-Democrat and that most pro-Republican users are mid-influential and likely to be human (low botness).
Name(s):Susanne Girard
Video Broadcast
Video Broadcast:YesTo Location:Saarbr├╝cken
To Building:E1 5To Room:029
Meeting ID:
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Attachments, File(s):
Susanne Girard/MPI-SWS, 04/19/2018 10:57 AM
Last modified:
Uwe Brahm/MPII/DE, 05/03/2018 07:01 AM
  • Susanne Girard, 05/02/2018 03:11 PM
  • Susanne Girard, 04/19/2018 11:01 AM -- Created document.