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Spread & networksDetection methods "This is Fake! Shared it by Mistake": Assessing the Intent of Fake News SpreadersThis work is the first to systematically assess whether individuals spread fake news intentionally or unintentionally. The authors propose an influence graph that captures psychological (internal) and social (external) factors driving unintentional spreading, validate it on two datasets with manually-annotated spreader intent, and… Zhou et al. · ACM Web Conference 2022 · 2022

LLMs & generative AIDetection methods All the News That's Fit to Fabricate: AI-Generated Text as a Tool of Media MisinformationThree experiments investigate whether AI-generated news text can be credible and influence public opinion. The authors find that GPT-2 models (355M to 1.5B parameters) generate text nearly indistinguishable from human-written baseline news, particularly when politically congruent stories are paired with partisan audiences. Kreps et al. · Journal of Experimental Political Science · 2022

LLMs & generative AIFact-checking & verification Atlas: Few-shot Learning with Retrieval Augmented Language ModelsATLAS is a retrieval-augmented language model that achieves strong few-shot performance on knowledge-intensive tasks by jointly training a dense retriever with a sequence-to-sequence language model. Unlike standard language models that must memorize knowledge, ATLAS retrieves relevant documents at both pre-training and fine-tuning… Izacard et al. · arXiv · 2022

LLMs & generative AIDetection methods Discovering Latent Knowledge in Language Models Without SupervisionLanguage models trained with standard objectives (imitation, reward-based) can output false statements even when they internally "know" the truth. This paper proposes Contrast-Consistent Search (CCS), an unsupervised method that exploits truth's logical structure—a statement and its negation cannot both be true—to recover latent… Burns 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

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

LLMs & generative AIDatasets & evaluation Discovering Language Model Behaviors with Model-Written EvaluationsRather than manually creating evaluation datasets, this work proposes using language models themselves to generate high-quality evaluations for testing diverse LM behaviors. The authors generate 154 datasets testing behaviors spanning personality, political views, ethics, and goal-seeking. Perez et al. · 2022

LLMs & generative AIDetection methods Causal Machine Learning: A Survey and Open ProblemsComprehensive survey of causal machine learning (CausalML), which formalizes data generation as a structural causal model to reason about interventions and counterfactuals. Categorizes 191 pages of CausalML work into five groups—causal supervised learning, causal generative modeling, causal explanations, causal fairness, and causal… Kaddour et al. · arXiv · 2022

Platforms, policy & societyDetection methods A Comprehensive Survey on Trustworthy Recommender SystemsRecommender systems, while beneficial to users, can amplify harmful content, introduce discrimination, and enable adversarial attacks. This survey provides a comprehensive framework for trustworthy recommender systems across six critical dimensions: Safety & Robustness (defense against adversarial attacks), Non-discrimination & Fairness… Fan et al. · ACM Computing Surveys · 2022

Detection methodsLLMs & generative AI Toxicity Detection with Generative Prompt-based InferenceThis paper proposes a generative variant of zero-shot prompt-based toxicity detection using language models, comparing it against discriminative approaches. By formulating the problem as estimating the probability of generating a test instance conditioned on toxicity-guided prompts, the method achieves better performance than… Wang & Chang · arXiv · 2022

Deepfakes & multimodalDatasets & evaluation Deepfake Detection: A Systematic Literature ReviewSystematic review of 112 deepfake detection studies (2018–2020) categorizing techniques into deep learning, machine learning, statistical, and blockchain methods. Deep learning approaches (77% of studies, primarily CNNs) significantly outperform non-deep methods, achieving ~89.7% mean accuracy. Rana et al. · IEEE Access · 2022

Datasets & evaluationDetection methods NELA-GT-2022: A Large Multi-Labelled News Dataset for The Study of Misinformation in News ArticlesPresents the fifth NELA-GT dataset release: 1.78 million news articles from 361 outlets spanning all of 2022, with source-level veracity labels from Media Bias/Fact Check and embedded tweet data. Focuses on stabilizing collection infrastructure to achieve near-complete temporal coverage, enabling robust benchmarking of fake news… Gruppi et al. · arXiv · 2022

LLMs & generative AIDetection methods Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated TextDugan et al. investigate human ability to detect boundaries where text transitions from human-written to machine-generated, a more realistic and challenging task than binary fake/real classification. They introduce the RoFT (Real or Fake Text) game platform for crowdsourced annotation and release a dataset of 21,000+ annotations across… Dugan et al. · arXiv · 2022

Spread & networksPlatforms, policy & society Right-Wing YouTube: A Supply and Demand PerspectiveYouTube has become the dominant platform for right-wing political content in the US, driven by supply-side affordances (low creation barriers, monetization, algorithmic recommendation) and demand-side audience appetite for alternative media. Using longitudinal analysis of creator clusters (Conservative, Alt-Lite, Alt-Right), the authors… Munger & Phillips · The International Journal of Press/Politics · 2022

Psychology & interventionsFact-checking & verification The psychological drivers of misinformation belief and its resistance to correctionThis comprehensive review establishes that misinformation's power lies not in exposure but in how cognitive, social, and affective factors shape belief formation and persist even after correction. False beliefs arise from intuitive thinking, memory failures, emotional content, in-group bias, and source credibility judgments; corrections… Ecker et al. · Nature Reviews Psychology · 2022

Psychology & interventionsPlatforms, policy & society Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USARandomized controlled trial on 8,001 respondents (4,000 UK, 4,001 USA) measuring causal impact of exposure to COVID-19 vaccine misinformation. Exposure to misinformation reduced vaccination intent by 6.2 percentage points in the UK and 6.4 in the USA relative to exposure to factual information. Loomba et al. · Nature Human Behaviour · 2021

Fact-checking & verificationDetection methods A Survey on Automated Fact-CheckingThis comprehensive survey unifies automated fact-checking research under a four-stage NLP framework: claim detection (identifying check-worthy claims), evidence retrieval (finding supporting/refuting information), verdict prediction (assigning truth labels), and justification production (explaining decisions). The authors review 25+… Guo et al. · arXiv · 2021

Spread & networksDatasets & evaluation Prevalence of Health Misinformation on Social Media: Systematic ReviewSystematic review of 69 studies examining health misinformation prevalence across social media platforms. Vaccines, drugs, and smoking are the most-studied topics; Twitter is the primary platform for misinformation about smoking and substances, while vaccines are discussed broadly across platforms. Suarez-Lledo & Alvarez-Galvez · Journal of Medical Internet Research · 2021

Psychology & interventionsPlatforms, policy & society Shifting attention to accuracy can reduce misinformation onlineThe paper shows through six online experiments and a Twitter field trial that simply drawing people's attention to accuracy can substantially reduce their sharing of misinformation. When asked to rate the accuracy of headlines before deciding whether to share them, participants share more accurate content; a Twitter field experiment… Pennycook et al. · Nature · 2021

Detection methodsLLMs & generative AI Belief Evolution Network-based Probability Transformation and FusionThis paper introduces a Belief Evolution Network (BEN) to model the evolution of belief functions and proposes a new Full Causality Probability Transformation (FCPT) method for converting belief mass assignments to probability distributions. The authors also propose FCPT-based fusion rules that outperform traditional Dempster's Rule of… Zhou et al. · arXiv · 2021

Deepfakes & multimodalDetection methods Multimodal Emergent Fake News Detection via Meta Neural Process NetworksExisting fake news detectors trained on labeled data perform poorly on novel, emergent events due to domain shift. The paper proposes MetaFEND, which combines meta-learning and neural processes to leverage a handful of labeled posts from a new event to quickly adapt a detection model. Wang et al. · ACM SIGKDD Conference on Knowledge Discovery… · 2021

Datasets & evaluationDetection methods Assessing the Quality of the Datasets by Identifying Mislabeled SamplesDeep learning models with high capacity often memorize label noise, degrading generalization. This paper proposes AQUAVS, a supervised variational autoencoder that identifies mislabeled samples via a "noise score" computed from outliers in latent space representations. Pulastya et al. · arXiv · 2021

Detection methodsLLMs & generative AI Perceived and Intended Sarcasm Detection with Graph Attention NetworksSarcasm detection requires understanding both the author's intent and the audience's perception. This work proposes a graph attention network (GAT) that jointly models user historical context and conversational social network information on Twitter, achieving state-of-the-art results on the SPIRS dataset and showing that detecting… Plepi & Flek · arXiv · 2021

Detection methodsDatasets & evaluation Misinformation Detection on YouTube Using Video CaptionsThis work proposes using video captions as text features for classifying YouTube videos as misinformation, debunking misinformation, or neutral content. Across five conspiracy topics (vaccines, 9/11, chemtrails, moon landing, flat earth), the authors achieve 0.85–0.90 F1-score on three-class classification and 0.92–0.95 F1-score when… Jagtap et al. · arXiv · 2021