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

arXiv 1802.04291 ↗First posted 12 Feb 2018cs.SI cs.CYcited by 30

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Abstract (arXiv)

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.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Feb 2018.

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Citing papers per year: 2021: 5; 2022: 7; 2023: 7; 2024: 7; 2025: 3; 2026: 1.

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  1. 2023Advancing Beyond Identification: Multi-bit Watermark for Large Language Modelscited by 51
  2. 2023Open, Closed, or Small Language Models for Text Classification?cited by 34
  3. 2022Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networkscited by 13
  4. 2023Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detectioncited by 13
  5. 2022Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Redditcited by 11
  6. 2024AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Imagescited by 10
  7. 2022Weakly Supervised Learning for Analyzing Political Campaigns on Facebookcited by 8
  8. 2024Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communitiescited by 7
  9. 2025Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operationscited by 6
  10. 2022Multimodal and Explainable Internet Meme Classificationcited by 5
  11. 2023Partial Mobilization: Tracking Multilingual Information Flows Amongst Russian Media Outlets and Telegramcited by 5
  12. 2021Challenges and Considerations with Code-Mixed NLP for Multilingual Societiescited by 4
18 more
  1. 2021Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Studycited by 3
  2. 2025Social Identity in Human-Agent Interaction: A Primercited by 3
  3. 2023Analyzing the Strategy of Propaganda using Inverse Reinforcement Learning: Evidence from the 2022 Russian Invasion of Ukrainecited by 2
  4. 2021Dynamic Social Media Monitoring for Fast-Evolving Online Discussionscited by 1
  5. 2021SOMPS-Net : Attention based social graph framework for early detection of fake health newscited by 1
  6. 2022Political Communities on Twitter: Case Study of the 2022 French Presidential Electioncited by 1
  7. 2022Machine Learning-based Automatic Annotation and Detection of COVID-19 Fake Newscited by 1
  8. 2024Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccinationcited by 1
  9. 2021"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendationcited by 0
  10. 2022Regional Differences in Information Privacy Concerns After the Facebook-Cambridge Analytica Data Scandalcited by 0
  11. 2023BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipelinecited by 0
  12. 2023Party Prediction for Twittercited by 0
  13. 2024Clustering Document Parts: Detecting and Characterizing Influence Campaigns from Documentscited by 0
  14. 2024Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networkscited by 0
  15. 2024Large Language Models Reveal Information Operation Goals, Tactics, and Narrative Framescited by 0
  16. 2024Posts of Peril: Detecting Information About Hazards in Textcited by 0
  17. 2025What is a Social Media Bot? A Global Comparison of Bot and Human Characteristicscited by 0
  18. 2026Manufactured Divisiveness: Decomposing the Hostile Content of Seven Social Media Influence Operationscited by 0