Skip to content
GUIDEUnderstanding fake news, misinformation & disinformation

Bots & coordinated campaigns

Social bots, trolls, state-backed operations, and accounts that act in concert.

56 pages Filter and search within this area
Platforms, policy & societySpread & networks Investigating Media Manipulation and DisinformationResearchers at Harvard Kennedy School present frameworks and practical tools for investigating online media manipulation. Joan Donovan introduces mediammanipulation.org's encyclopedia of manipulation tactics and a detailed case study of how the COVID lab-leak disinformation campaign exploited academic preprint archives. Donovan · 2026 Deepfakes & multimodalLLMs & generative AI Detector-Guided Vocoder Training for Adversarial Text-to-Speech SynthesisThis paper presents a method to generate synthetic speech that evades deepfake detectors by integrating detector loss directly into vocoder training, rather than applying post-hoc perturbations. The approach fine-tunes a BigVGAN vocoder while freezing the acoustic model and detector, generating adversarial speech that retains high… Cui et al. · arXiv · 2026 Bots & coordinated campaignsDatasets & evaluation Statistical Detectability of CoordinationThis paper formulates coordination detection as a hypothesis-testing problem on account pairs, modeling coordination as a planted submatrix. The authors derive statistical and computational detection limits, test four different null models on 26 information-operation datasets across 7 modalities, and show that multi-modal aggregation is… Iannucci et al. · arXiv · 2026 Bots & coordinated campaignsSpread & networks Birds of a Feather Flock Together: Network-Based Detection of Coordinated Disinformation Campaigns on TelegramA graph-based methodology discovers coordinated disinformation campaigns on Telegram by analyzing message-sharing patterns rather than content, requiring only structural relationships. The authors identified 55 disinformation channels and over 86,000 identical message chains spanning multiple channels, revealing a highly synchronized… Tsagkarakis et al. · SECRYPT · 2026 Bots & coordinated campaignsDatasets & evaluation Setting the clock: Evaluating temporal window parameters for coordinated behavior detectionThe paper systematically evaluates how temporal window parameters (length and stride) affect detection of coordinated communities in co-repost networks. Using 14 labeled information operation datasets, the authors find that window length is a central modeling parameter: short windows (seconds to minutes) detect tight, highly synchronized… Panayiotou et al. · AIDEM Workshop, ECML-PKDD 2026 · 2026 Platforms, policy & societySpread & networks Control-Theoretic Content ModerationContent moderation at the platform level requires balancing multiple competing objectives (reducing harmful content, preserving engagement, maintaining activity). This paper formulates platform-level moderation as a feedback control problem with a multi-objective loss function, proposes control-theoretic moderators (including… Tessa et al. · arXiv · 2026 Bots & coordinated campaignsSpread & networks Social bots weaken activist cohesionSocial bots during protest peaks weaken the human relational infrastructure of activism. Analyzing 2.5M tweets from the 2020 Black Lives Matter protests, the authors show that networks more exposed to bot infiltration suffered steeper declines in human cohesion (triadic closure and edge density) after the peak, with effects concentrated… Li et al. · arXiv · 2026 LLMs & generative AIPlatforms, policy & society Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World DataThis paper presents the first comprehensive taxonomy of generative AI misuse tactics grounded in 191 real-world documented incidents (January 2023–March 2024). The authors identify two broad categories—exploitation of GenAI capabilities and compromise of GenAI systems—with 18 distinct tactics. Marchal et al. · arXiv · 2024 LLMs & generative AIPlatforms, policy & society Generative Language Models and Automated Influence Operations: Emerging Threats and Potential MitigationsGenerative language models like GPT-3 and ChatGPT could significantly expand the scale and scope of influence operations by automating high-quality propaganda generation, lowering barriers to entry for new propagandists, and enabling novel tactics. The authors use an Actor-Behavior-Content (ABC) framework to identify how these models… Goldstein et al. · arXiv · 2023 LLMs & generative AIPlatforms, policy & society Universal and Transferable Adversarial Attacks on Aligned Language ModelsDespite alignment training, modern language models can be induced to generate harmful content via automatically-discovered adversarial suffixes. The authors propose Greedy Coordinate Gradient (GCG) search, which generates a single universal suffix that transfers across multiple models and prompts with high success rates (88% on… Zou et al. · arXiv · 2023 LLMs & generative AIDetection methods DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability CurvatureThis paper identifies that text sampled from language models tends to occupy negative-curvature regions of the model's log-probability landscape, whereas human-written text occupies more neutral regions. DetectGPT leverages this observation to detect machine-generated text in a zero-shot manner by analyzing probability curvature, without… Mitchell et al. · 40th International Conference on Machine… · 2023 LLMs & generative AIBots & coordinated campaigns Red Teaming Language Models with Language ModelsThis paper demonstrates that language models can automatically discover a wide range of harmful behaviors in other language models through "red teaming"—using one LM to generate adversarial test cases that probe another LM's vulnerabilities. The authors explore multiple generation strategies (zero-shot, few-shot, supervised learning,… Perez et al. · arXiv · 2022 Bots & coordinated campaignsDatasets & evaluation Botometer 101: Social bot practicum for computational social scientistsThis tutorial introduces Botometer, a supervised machine learning classifier for detecting social bots on Twitter. The authors explain how Botometer extracts features from account profiles and tweets, describe multiple versions optimized for different use cases (including BotometerLite for large-scale analysis), and provide practical… Yang et al. · arXiv · 2022 Platforms, policy & societySpread & networks How to Demolish the Disinformation InfrastructureKate Starbird, co-principal investigator at the University of Washington's Center for an Informed Public, discusses the state of disinformation in the aftermath of platform removals (Trump, QAnon), focusing on how moderation actions redirect harmful activity to alternative ("alt-tech") platforms rather than eliminating it. She emphasizes… Starbird · The Open Mind · 2021 Platforms, policy & societyBots & coordinated campaigns The Threat of Offensive AI to OrganizationsA comprehensive survey identifying 33 offensive AI capabilities that adversaries can use to attack organizations, organized into seven categories: automation, campaign resilience, credential theft, exploit development, information gathering, social engineering, and stealth. Through a user study with 22 experts from academia and industry,… Mirsky et al. · ACM Computing Surveys · 2021 Bots & coordinated campaignsSpread & networks Uncovering Coordinated Networks on Social Media: Methods and Case StudiesThis paper proposes a general, unsupervised, network-based methodology to detect coordinated inauthentic behavior on social media. The approach identifies behavioral traces (handle changes, image sharing, hashtag sequences, co-retweets, synchronized actions) and constructs bipartite networks to discover clusters of accounts likely… Pacheco et al. · ICWSM · 2020 Spread & networksBots & coordinated campaigns On the Detection of Disinformation Campaign Activity with Network AnalysisState-sponsored disinformation campaigns on Twitter exhibit distinct coordination patterns (shared messages, hashtags, URLs, retweet cascades) that differ from legitimate coordinated activity. The paper proposes using coordination network features—extracted from daily account interactions—to train a binary classifier that detects… Vargas et al. · Cloud Computing Security Workshop · 2020 Bots & coordinated campaignsDatasets & evaluation A Decade of Social Bot DetectionA comprehensive review of social bot detection research from 2010 to 2020, documenting the evolution from supervised individual-account detection to group-level approaches targeting coordinated inauthentic behavior. The paper traces the escalating "arms race" between bot developers and detection researchers, illustrates bots' documented… Cresci · Communications of the ACM · 2020 Datasets & evaluationSpread & networks Characterizing COVID-19 Misinformation Communities Using a Novel Twitter DatasetThis paper presents CMU-MisCOV19, a manually annotated Twitter dataset of 4,573 COVID-19 tweets across 17 categories, and characterizes misinformed versus informed online communities. Key findings: misinformed communities are significantly denser (higher network density), contain more bots (19% vs 11%), and demonstrate higher… Memon & Carley · arXiv · 2020 Bots & coordinated campaignsDetection methods Detection of Novel Social Bots by Ensembles of Specialized ClassifiersDifferent types of social bots exhibit heterogeneous behavioral features; training a single classifier on all bots fails to generalize to novel bot types. The authors propose ESC (Ensemble of Specialized Classifiers), which trains separate classifiers for each bot class and combines their decisions via a maximum rule, achieving a 56%… Sayyadiharikandeh et al. · arXiv · 2020 Bots & coordinated campaignsPlatforms, policy & society Assessing the Russian Internet Research Agency's impact on the political attitudes and behaviors of American Twitter users in late 2017Combining longitudinal survey data from 1,239 partisan Twitter users with nonpublic IRA account data, Bail et al. find no evidence that interaction with Russian Internet Research Agency accounts substantially impacted six measures of political attitudes (affective/ideological polarization) or behaviors (political engagement, network… Bail et al. · National Academy of Sciences · 2020 Bots & coordinated campaignsSpread & networks It takes a village to manipulate the media: coordinated link sharing behavior during 2018 and 2019 Italian electionsCoordinated networks of Facebook pages and groups substantially amplified problematic news during Italian elections: coordinated entities shared problematic domains 1.79×–2.22× more frequently than uncoordinated ones. The paper introduces a method to detect "coordinated inauthentic behavior" by identifying entities that… Giglietto et al. · Information, Communication & Society · 2020 Bots & coordinated campaignsPlatforms, policy & society Cross-Platform State Propaganda: Russian Trolls on Twitter and YouTube during the 2016 U.S. Presidential ElectionThis paper analyzes 1,052 Russian IRA Twitter accounts to understand their propaganda strategy during the 2016 U.S. presidential election. By examining hyperlinks to news stories and YouTube videos, the authors find IRA accounts linked to conservative sources more frequently than liberal ones, consistent with supporting Trump. Golovchenko et al. · The International Journal of Press/Politics · 2020 Bots & coordinated campaignsPlatforms, policy & society Troll Factories: Manufacturing Specialized Disinformation on TwitterLinvill and Warren analyze 9.04 million tweets from Russia's Internet Research Agency (IRA) spanning 2009–2018, identifying five specialized account categories—Right Troll, Left Troll, News Feed, Hashtag Gamer, and Fearmonger—each with distinct behavioral patterns, temporal deployment strategies, and communication styles. The authors… Linvill & Warren · Political Communication · 2020 Platforms, policy & societyBots & coordinated campaigns Facebook's role in Brexit — and the threat to democracyInvestigative journalist Carole Cadwalladr exposes the systematic abuse of Facebook as an instrument for election interference during the 2016 Brexit referendum. She documents illegal campaign spending, microtargeted disinformation, Cambridge Analytica's harvesting of 87 million user profiles, and traces connections between Brexit and… Cadwalladr · 2019 Spread & networksBots & coordinated campaigns 5 Reasons Why Social Networks Make Us Vulnerable to MisinformationMenczer presents five fundamental factors that make social networks inherently vulnerable to misinformation spread. Information and misinformation propagate through networks at equivalent rates due to structural properties of scale-free networks combined with human cognitive limitations. Menczer · Aula Rostagni, University of Padova · 2019 Spread & networksPlatforms, policy & society Auditing Radicalization Pathways on YouTubeFirst large-scale quantitative audit of user radicalization on YouTube. The authors tracked 349 channels across three ideological communities (Intellectual Dark Web, Alt-lite, Alt-right) and found strong evidence that users systematically migrate toward more extreme content. Horta Ribeiro et al. · ACM 2020 Conference on Fairness,… · 2019 LLMs & generative AIDetection methods Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based DetectionThe paper demonstrates a practical attack where low-skilled adversaries can generate high-quality fake product reviews at scale by fine-tuning publicly available GPT-2 models on real review datasets. A two-step approach—generation plus BERT-based validation to preserve sentiment—produces reviews indistinguishable from genuine ones. Adelani et al. · arXiv · 2019 Bots & coordinated campaignsDetection methods Better Safe Than Sorry: an Adversarial Approach to improve Social Bot DetectionProposes GenBot, a novel genetic algorithm that synthesizes evolved versions of existing spambots by encoding behavioral timelines as "digital DNA" sequences and iteratively evolving them to resemble legitimate user behavior. Tests the evolved bots against three state-of-the-art detection systems and finds they largely evade detection… Cresci et al. · ACM Web Science Conference · 2019 Bots & coordinated campaignsDatasets & evaluation Arming the public with artificial intelligence to counter social botsThis paper reviews social bot types, their impact on online discourse, and detection methods, then presents Botometer as a case study in how detection tools are used and interpreted by the public. The authors conducted a user experience survey (731 participants) revealing that while users find the tool useful, they struggle with… Yang et al. · arXiv · 2019 Bots & coordinated campaignsDatasets & evaluation Scalable and Generalizable Social Bot Detection through Data SelectionThis paper addresses scalability and generalization challenges in social bot detection by proposing a framework using only 20 user metadata features, enabling real-time analysis of the full Twitter stream. The key insight is that strategic data selection—training on a carefully chosen subset of 13 compiled datasets—achieves better… Yang et al. · arXiv · 2019 Bots & coordinated campaignsDeepfakes & multimodal Characterizing the Use of Images in State-Sponsored Information Warfare Operations by Russian Trolls on TwitterLarge-scale analysis of 1.8M images posted by ~3.6K Russian state-sponsored Twitter accounts (IRA-controlled). Russian trolls strategically deployed images and memes to maximize political influence, with political imagery 2+ times more effective than general content. Zannettou et al. · AAAI Conference on Web and Social Media · 2019 Bots & coordinated campaignsPlatforms, policy & society Coordinating a Multi-Platform Disinformation Campaign: Internet Research Agency Activity on Three U.S. Social Media Platforms, 2015 to 2017This paper analyzes Russian Internet Research Agency activity across Facebook, Twitter, and Reddit from 2015–2017 using vector autoregression models on 3,126 Facebook ads, 1.9M tweets, and 12,603 Reddit posts. Key finding: Reddit activity influenced subsequent Twitter activity (a "trial balloon" effect), but the IRA's Twitter engagement… Lukito · Political Communication · 2019 Spread & networksBots & coordinated campaigns A Study of Misinformation in WhatsApp groups with a focus on the Brazilian Presidential ElectionsStudy of 130 public WhatsApp groups during the 2018 Brazilian presidential election found that 13.1% of shared links pointed to junk news sources, with 40% of links directing to YouTube—a platform used strategically to bypass platform moderation. Videos and images dominated message content (33% of all messages) and were heavily… Machado et al. · The Web Conference · 2019 Bots & coordinated campaignsPlatforms, policy & society Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the WebLarge-scale empirical study of 2.7K Russian state-sponsored troll accounts and 27K tweets (Jan 2016–Sep 2017) reveals minimal direct Twitter influence but significant content amplification through RT news outlet. Trolls actively target political events, adopt multiple identities, concentrate geographically in USA/Germany/Eastern Europe,… Zannettou et al. · arXiv · 2018 Spread & networksBots & coordinated campaigns On the Origins of Memes by Means of Fringe Web CommunitiesLarge-scale empirical study of how image memes originate and propagate across fringe and mainstream Web communities. Analyzes 160M images posted to Twitter, Reddit, /pol/, and Gab (2016–2017) using perceptual hashing, clustering, and custom distance metrics. Zannettou et al. · IMC 2018 · 2018 Bots & coordinated campaignsSpread & networks Who Let The Trolls Out? Towards Understanding State-Sponsored TrollsComparative analysis of 934 Russian and 770 Iranian state-sponsored trolls across Twitter and Reddit (10M posts) reveals distinct operational patterns: Russian trolls prioritized URL amplification (retweets, mentions) and pro-Trump messaging; Iranian trolls pursued regional and anti-Trump narratives motivated by real-world events. Both… Zannettou et al. · arXiv · 2018 Spread & networksBots & coordinated campaigns Anatomy of an online misinformation networkA network analysis of misinformation and fact-checking spread during the 2016 US Presidential Election reveals stark segregation between the two communities on Twitter. The core of the network is dominated by misinformation spreaders; fact-checking nearly disappears in the densest regions. Shao et al. · arXiv · 2018 Spread & networksBots & coordinated campaigns Influencers in Polarized Political Networks on TwitterThis paper identifies three types of influencers in polarized political Twitter conversations during Brazil's 2016 Dilma Rousseff impeachment: "opinion leaders" who shape discourse within ideological groups, "informational influencers" (media/institutions) drawn into one camp, and "activists" who amplify like-minded messages. The… Soares et al. · SMSociety · 2018 Bots & coordinated campaignsSpread & networks Bots increase exposure to negative and inflammatory content in online social systemsSocial bots during the 2017 Catalan independence referendum strategically targeted influential humans to amplify negative and inflammatory content. Operating from network peripheries, bots coordinated sentiment-driven amplification that exacerbated polarization, with approximately one in three referendum-related tweets generated by bots. Stella et al. · National Academy of Sciences · 2018 Spread & networksBots & coordinated campaigns The Truth About False NewsA presentation discussing findings from a large-scale longitudinal study of false and true news spread on Twitter (2006–2017). The research found that false news spreads further, faster, and more broadly than truth by orders of magnitude—not primarily due to bots, but because humans are more susceptible to sharing novel information, and… Aral · 2017 Bots & coordinated campaignsSpread & networks Social Bots and the Spread of Fake NewsA presentation on how social bots amplify misinformation spread on Twitter and other social platforms. The speaker discusses bot detection techniques (network structure, user behavior, temporal patterns), case studies of coordinated bot campaigns (2010–2016 elections, vaccine debates, Brexit), findings showing bots act as "igniters" in… Menczer · 2017 Bots & coordinated campaignsDetection methods The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms RaceThis paper provides empirical evidence of a paradigm shift in spambot design on Twitter, where new "social spambots" closely mimic genuine user behavior and successfully evade detection. Neither Twitter's automated systems, human annotators, nor established academic detection tools can effectively identify these accounts; however,… Cresci et al. · WWW 2017 Companion · 2017 Spread & networksBots & coordinated campaigns The spread of low-credibility content by social botsEmpirical analysis of 14 million Twitter messages during the 2016 U.S. election finds social bots disproportionately amplify low-credibility content: though bots comprise only 6% of accounts sharing such content, they account for 31% of tweets. Bots employ two strategies—rapid early amplification and targeting of influential users—making… Shao et al. · arXiv · 2017 Bots & coordinated campaignsDatasets & evaluation Online Human-Bot Interactions: Detection, Estimation, and CharacterizationSocial media accounts controlled by software can mimic human behavior while executing coordinated campaigns. This paper develops a machine-learning framework extracting 1,150 behavioral features from Twitter account metadata, posting patterns, and social networks to classify bots with 95% accuracy on honeypot data. Varol et al. · arXiv · 2017 Bots & coordinated campaignsPlatforms, policy & society Detecting Bots on Russian Political TwitterAutomated Twitter accounts (bots) represent 50% of activity in Russian political discourse. Using supervised ensemble classifiers on 3.8M accounts and 18M tweets from 2014–2015, the authors achieve 95% precision in detecting bots while maintaining 75% recall. Stukal et al. · Big Data · 2017 Spread & networksBots & coordinated campaigns The Spread of True and False News OnlineAnalyzes 126,000 verified true and false news cascades from Twitter (2006–2017) covering 3 million users and 4.5 million shares. False news diffuses significantly farther, faster, deeper, and more broadly than truth across all categories, with false political news spreading fastest. Vosoughi et al. · MIT Initiative on the Digital Economy… · 2017 Bots & coordinated campaignsDetection methods DNA-Inspired Online Behavioral Modeling and Its Application to Spambot DetectionProposes a novel methodology to characterize online user behavior using DNA-inspired digital sequences, where each user action (tweet type, content features) is encoded as a character in a string. Applies standard bioinformatic DNA analysis techniques—specifically longest common substring (LCS) analysis—to detect spambots on Twitter by… Cresci et al. · IEEE Intelligent Systems · 2016 Bots & coordinated campaignsDatasets & evaluation BotOrNot: A System to Evaluate Social BotsBotOrNot is a publicly available service and API that evaluates whether a Twitter account is controlled by a human or automated bot. Using 1,000+ features across six categories (network, user, friends, temporal, content, sentiment), the system applies Random Forest classification to achieve 0.95 AUC on a labeled dataset of 15k bots and… Davis et al. · 25th International Conference Companion on… · 2016 Bots & coordinated campaignsDetection methods The Rise of Social BotsSocial bots—automated software agents in social media—are increasingly sophisticated and pose real threats including misinformation spread, trend manipulation, and election interference. This paper characterizes modern bot behavior, surveys detection challenges, and proposes a taxonomy of detection approaches: graph-based methods… Ferrara et al. · ACM Transactions on the Web · 2015 Spread & networksBots & coordinated campaigns Detecting and Tracking the Spread of Astroturf Memes in Microblog StreamsPolitical campaigns increasingly use social media to amplify deceptive memes that mimic organic grassroots behavior—termed "astroturfing." This paper introduces the Truthy system, which analyzes meme diffusion networks on Twitter to detect orchestrated campaigns through network topology and sentiment analysis, achieving ~90%… Ratkiewicz et al. · arXiv · 2010 Platforms, policy & societyBots & coordinated campaigns Partisanship, Propaganda, and Disinformation: Online Media and the 2016 U.S. Presidential ElectionThis large-scale analysis of mainstream and social media coverage during the 2016 U.S. presidential election documents asymmetric partisan structures: conservative media formed an insulated, hyperpartisan ecosystem centered on far-right outlets like Breitbart, while liberal media remained anchored in traditional journalistic practices.… Faris et al. Bots & coordinated campaignsPlatforms, policy & society The Global Disinformation Order: 2019 Global Inventory of Organised Social Media ManipulationA comprehensive inventory of government and political-party organized social media manipulation campaigns (cyber troops) across 70 countries. The report catalogs organizational structures, strategies, platforms, and resources deployed for computational propaganda; finds organized manipulation in 70 countries (up from 28 in 2017), with… Bradshaw & Howard Spread & networksBots & coordinated campaigns The Spread of Misinformation in Social MediaMenczer presents a framework for understanding misinformation spread through social networks as an inevitable consequence of network structure and human cognitive limitations rather than platform design failures. He discusses how structural properties—scale-free topology, finite attention, information overload—create vulnerabilities… Menczer · OSoMe, Indiana University Platforms, policy & societyBots & coordinated campaigns How to Counter Russian Social Media Influence in Eastern EuropeRussia deploys sophisticated social media campaigns in Eastern Europe using state-funded media, coordinated troll and bot networks, fake hashtags, and nonattributed comments to sow dissent against neighboring governments and NATO. The RAND study analyzed social media data and expert interviews to identify counter-strategies: accelerating… Helmus et al. Spread & networksBots & coordinated campaigns Media Manipulation and Disinformation OnlineInternet subcultures including trolls, gamergaters, white nationalists, and conspiracy theorists exploit online platforms' vulnerabilities—algorithmic amplification, engagement metrics, and participatory culture—to manipulate news frames and spread disinformation. These diverse groups employ coordinated tactics (memes, bots, strategic… Marwick & Lewis