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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 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 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 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

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

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

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

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 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

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

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 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

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

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 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 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

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

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