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Location:The MITRE CorporationC Building, Room 1C-103202 Burlington RoadBedford, MAdirections included as an attachment directions.pdfTitle:The Impact of Bots on Opinions in Social NetworksSpeak: Tauhid ZamanAbstract:We present an analysis of the impact of automated accounts, or bots, on opinions in a social network. We model the opinions using a variant of the famous DeGroot model, which connects opinions with network structure. We find a nontrivial correlation between opinions based on this network model and based on the content of tweets of Twitter users discussing the 2016 U.S. presidential election between Hillary Clinton and Donald Trump, providing evidence supporting the validity of the model. We then utilize the network model to predict what the opinions would have been if the network did not contain any bots which may be trying to manipulate opinions. Using a bot detection algorithm, we identify bot accounts which comprise less than 1% of the network. By analyzing the bot posts, we find that there are twice as many bots supporting Donald Trump as there are supporting Hillary Clinton. We remove the bots from the network and recalculate the opinions using the network model. We find that the bots produce a significant shift in the opinions, with the Clinton bots producing almost twice as large a change as the Trump bots, despite being fewer in number. Analysis of the bot behavior reveals that the large shift is due to the fact that the bots post one hundred times more frequently than humans. The asymmetry in the opinion shift is due to the fact that the Clinton bots post 50% more frequently than the Trump bots. Our results suggest a small number of highly active bots in a social network can have a disproportionate impact on opinions.Bio: Tauhid is an Associate Professor of Operations Management at the MIT Sloan School of Management. He received his BS, MEng, and PhD degrees in electrical engineering and computer science from MIT. His research focuses on solving operational problems involving social network data using probabilistic models, network algorithms, and modern statistical methods. Some of the topics he studies in the social networks space include predicting the popularity of content, finding online extremists, and geo-locating users. His broader interests cover data driven approaches to investing in startup companies, non-traditional choice modeling, algorithmic sports betting, and biometric data. His work has been featured in the Wall Street Journal, Wired, Mashable, the LA Times, and Time Magazine.
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