Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign

Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign. In Proceedings of The international conference series on Advances in Social Network Analysis and Mining (ASONAM-2018), 2018.

Paper abstract bibtex 2 downloads

Until recently, social media was seen to promote democratic discourse on social and political issues. However, this powerful communication platform has come under scrutiny for allowing hostile actors to exploit online discussions in an attempt to manipulate public opinion. A case in point is the ongoing U.S. Congress' investigation of Russian interference in the 2016 U.S. election campaign, with Russia accused of using trolls (malicious accounts created to manipulate) and bots to spread misinformation and politically biased information. In this study, we explore the effects of this manipulation campaign, taking a closer look at users who re-shared the posts produced on Twitter by the Russian troll accounts publicly disclosed by U.S. Congress investigation. We collected a dataset with over 43 million election-related posts shared on Twitter between September 16 and October 21, 2016, by about 5.7 million distinct users. This dataset included accounts associated with the identified Russian trolls. We use label propagation to infer the ideology of all users based on the news sources they shared. This method enables us to classify a large number of users as liberal or conservative with precision and recall above 90%. Conservatives retweeted Russian trolls about 31 times more often than liberals and produced 36x more tweets. Additionally, most retweets of troll content originated from two Southern states: Tennessee and Texas. Using state-of-the-art bot detection techniques, we estimated that about 4.9% and 6.2% of liberal and conservative users respectively were bots. Text analysis on the content shared by trolls reveals that they had a mostly conservative, pro-Trump agenda. Although an ideologically broad swath of Twitter users was exposed to Russian Trolls in the period leading up to the 2016 U.S. Presidential election, it was mainly conservatives who helped amplify their message.

@inproceedings{Badawy2018asonam,
abstract = {Until recently, social media was seen to promote democratic discourse on
social and political issues. However, this powerful communication platform has
come under scrutiny for allowing hostile actors to exploit online discussions
in an attempt to manipulate public opinion. A case in point is the ongoing {U.S}.
Congress' investigation of Russian interference in the 2016 {U.S}. election
campaign, with Russia accused of using trolls (malicious accounts created to
manipulate) and bots to spread misinformation and politically biased
information. In this study, we explore the effects of this manipulation
campaign, taking a closer look at users who re-shared the posts produced on
Twitter by the Russian troll accounts publicly disclosed by {U.S}. Congress
investigation. We collected a dataset with over 43 million election-related
posts shared on Twitter between September 16 and October 21, 2016, by about 5.7
million distinct users. This dataset included accounts associated with the
identified Russian trolls. We use label propagation to infer the ideology of
all users based on the news sources they shared. This method enables us to
classify a large number of users as liberal or conservative with precision and
recall above 90\%. Conservatives retweeted Russian trolls about 31 times more
often than liberals and produced 36x more tweets. Additionally, most retweets
of troll content originated from two Southern states: Tennessee and Texas.
Using state-of-the-art bot detection techniques, we estimated that about 4.9\%
and 6.2\% of liberal and conservative users respectively were bots. Text
analysis on the content shared by trolls reveals that they had a mostly
conservative, {pro-Trump} agenda. Although an ideologically broad swath of
Twitter users was exposed to Russian Trolls in the period leading up to the
2016 {U.S}. Presidential election, it was mainly conservatives who helped amplify
their message.},
booktitle = {Proceedings of The international conference series on Advances in Social Network Analysis and Mining (ASONAM-2018)},
title = {Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign},
url = {http://arxiv.org/abs/1802.04291},
year = {2018},
keywords = {fake-news}
}

Downloads: 2

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We collected a dataset with over 43 million election-related posts shared on Twitter between September 16 and October 21, 2016, by about 5.7 million distinct users. This dataset included accounts associated with the identified Russian trolls. We use label propagation to infer the ideology of all users based on the news sources they shared. This method enables us to classify a large number of users as liberal or conservative with precision and recall above 90%. Conservatives retweeted Russian trolls about 31 times more often than liberals and produced 36x more tweets. Additionally, most retweets of troll content originated from two Southern states: Tennessee and Texas. Using state-of-the-art bot detection techniques, we estimated that about 4.9% and 6.2% of liberal and conservative users respectively were bots. Text analysis on the content shared by trolls reveals that they had a mostly conservative, pro-Trump agenda. 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A case in point is the ongoing {U.S}.\nCongress' investigation of Russian interference in the 2016 {U.S}. election\ncampaign, with Russia accused of using trolls (malicious accounts created to\nmanipulate) and bots to spread misinformation and politically biased\ninformation. In this study, we explore the effects of this manipulation\ncampaign, taking a closer look at users who re-shared the posts produced on\nTwitter by the Russian troll accounts publicly disclosed by {U.S}. Congress\ninvestigation. We collected a dataset with over 43 million election-related\nposts shared on Twitter between September 16 and October 21, 2016, by about 5.7\nmillion distinct users. This dataset included accounts associated with the\nidentified Russian trolls. We use label propagation to infer the ideology of\nall users based on the news sources they shared. This method enables us to\nclassify a large number of users as liberal or conservative with precision and\nrecall above 90\\%. 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