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Detection methodsLLMs & generative AI
The Limitations of Stylometry for Detecting Machine-Generated Fake NewsWhile stylometry effectively detects impersonation and provenance, it fails to distinguish between legitimate and malicious uses of language models for generating false content. LMs produce stylistically consistent text regardless of veracity, and adding even a single auto-generated sentence within human-written text evades stylometric…
LLMs & generative AIDetection methods
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLPIntroduces TextAttack, an open-source Python framework unifying 16 adversarial attacks from the literature into a modular architecture for testing and improving NLP model robustness. The framework decomposes attacks into four reusable components (goal function, constraints, transformation, search method), enables benchmarking attacks on…
LLMs & generative AIDetection methods
A Survey of the State of Explainable AI for Natural Language ProcessingRecent neural NLP models achieve strong performance but are often opaque. This survey systematically reviews explainable AI (XAI) research in NLP across 50 papers from major conferences (2013–2019), categorizing explanations by scope (local vs. global) and generation (self-explaining vs. post-hoc), detailing five major techniques…
Detection methodsLLMs & generative AI
Misinformation Has High PerplexityFalse claims have measurably higher perplexity when scored by a language model primed with truthful evidence, while true claims have lower perplexity. The authors exploit this observation to build an unsupervised debunker that achieves 75% accuracy on scientific COVID-19 claims with minimal labeled data, releasing two new annotated test…
LLMs & generative AIPlatforms, policy & society
Language Models are Few-Shot LearnersThis landmark paper introduces GPT-3, a 175-billion-parameter autoregressive language model, and demonstrates that it achieves strong performance on diverse NLP tasks through in-context few-shot learning without any task-specific fine-tuning. The model exhibits remarkable scaling properties where larger models dramatically improve…
Fact-checking & verificationDetection methods
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake NewsProposes a framework to automatically find fact-checking articles that address claims in original tweets. Uses a multimodal attention network (MAN) combining textual and visual content from tweets with fact-checking article text to rank candidate fact-checks; demonstrates that multimodal retrieval substantially outperforms text-only…
Detection methodsSpread & networks
Mining Disinformation and Fake News: Concepts, Methods, and Recent AdvancementsThis comprehensive tutorial surveys the landscape of fake news and disinformation research across three dimensions: understanding user engagement in information disorder, techniques for detecting and mitigating false content, and emerging challenges like fake news literacy and neural generation. The authors emphasize weak social…
Deepfakes & multimodalDetection methods
The Creation and Detection of Deepfakes: A SurveyComprehensive 38-page survey covering both creation and detection of deepfakes. Systematically reviews generative neural network architectures (GANs, autoencoders, CNNs, RNNs), technical approaches to reenactment, replacement, editing, and synthesis.
Spread & networksDetection methods
Rumor Detection on Social Media with Bi-Directional Graph Convolutional NetworksProposes Bi-GCN, a graph convolutional network approach for rumor detection that models both top-down propagation patterns (how rumors spread from source) and bottom-up dispersion patterns (how rumors diffuse within communities). By capturing both directional characteristics jointly and incorporating source post features at each layer,…
Detection methodsDatasets & evaluation
A Survey on Natural Language Processing for Fake News DetectionThis survey comprehensively reviews NLP methods for fake news detection, systematically comparing task formulations (classification vs. regression), nine benchmark datasets (LIAR, FEVER, FakeNewsNet, Twitter/SNS datasets), and five methodological approaches (preprocessing, machine learning models, rhetorical structure, evidence…
Detection methodsLLMs & generative AI
Fake News Early Detection: An Interdisciplinary StudyThis paper proposes a theory-driven, content-only model for fake news detection at publication time — before social-media propagation data exists. News articles are represented at four linguistic levels (lexicon, syntax, semantic, and discourse) using features grounded in social and forensic psychology; classical supervised classifiers…
LLMs & generative AIDetection methods
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLPIntroduces TextAttack, an open-source Python framework unifying 16 adversarial attacks from the literature into a modular architecture for testing and improving NLP model robustness. The framework decomposes attacks into four reusable components (goal function, constraints, transformation, search method), enables benchmarking attacks on…
LLMs & generative AIDetection methods
A Survey of the State of Explainable AI for Natural Language ProcessingRecent neural NLP models achieve strong performance but are often opaque. This survey systematically reviews explainable AI (XAI) research in NLP across 50 papers from major conferences (2013–2019), categorizing explanations by scope (local vs. global) and generation (self-explaining vs. post-hoc), detailing five major techniques…
Detection methodsLLMs & generative AI
Misinformation Has High PerplexityFalse claims have measurably higher perplexity when scored by a language model primed with truthful evidence, while true claims have lower perplexity. The authors exploit this observation to build an unsupervised debunker that achieves 75% accuracy on scientific COVID-19 claims with minimal labeled data, releasing two new annotated test…
LLMs & generative AIPlatforms, policy & society
Language Models are Few-Shot LearnersThis landmark paper introduces GPT-3, a 175-billion-parameter autoregressive language model, and demonstrates that it achieves strong performance on diverse NLP tasks through in-context few-shot learning without any task-specific fine-tuning. The model exhibits remarkable scaling properties where larger models dramatically improve…
Fact-checking & verificationDetection methods
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake NewsProposes a framework to automatically find fact-checking articles that address claims in original tweets. Uses a multimodal attention network (MAN) combining textual and visual content from tweets with fact-checking article text to rank candidate fact-checks; demonstrates that multimodal retrieval substantially outperforms text-only…
Detection methodsSpread & networks
Mining Disinformation and Fake News: Concepts, Methods, and Recent AdvancementsThis comprehensive tutorial surveys the landscape of fake news and disinformation research across three dimensions: understanding user engagement in information disorder, techniques for detecting and mitigating false content, and emerging challenges like fake news literacy and neural generation. The authors emphasize weak social…
Deepfakes & multimodalDetection methods
The Creation and Detection of Deepfakes: A SurveyComprehensive 38-page survey covering both creation and detection of deepfakes. Systematically reviews generative neural network architectures (GANs, autoencoders, CNNs, RNNs), technical approaches to reenactment, replacement, editing, and synthesis.
Spread & networksDetection methods
Rumor Detection on Social Media with Bi-Directional Graph Convolutional NetworksProposes Bi-GCN, a graph convolutional network approach for rumor detection that models both top-down propagation patterns (how rumors spread from source) and bottom-up dispersion patterns (how rumors diffuse within communities). By capturing both directional characteristics jointly and incorporating source post features at each layer,…
Detection methodsDatasets & evaluation
A Survey on Natural Language Processing for Fake News DetectionThis survey comprehensively reviews NLP methods for fake news detection, systematically comparing task formulations (classification vs. regression), nine benchmark datasets (LIAR, FEVER, FakeNewsNet, Twitter/SNS datasets), and five methodological approaches (preprocessing, machine learning models, rhetorical structure, evidence…
Detection methodsLLMs & generative AI
Fake News Early Detection: An Interdisciplinary StudyThis paper proposes a theory-driven, content-only model for fake news detection at publication time — before social-media propagation data exists. News articles are represented at four linguistic levels (lexicon, syntax, semantic, and discourse) using features grounded in social and forensic psychology; classical supervised classifiers…