Detection methods
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Detection methodsPlatforms, policy & society
Fake News Theories: Harnessing Disciplinary Insights for Computational Modeling, Detection, and ExplanationThis paper develops a theory-informed computational framework that translates cross-disciplinary social science theories into interpretable, measurable features for fake news detection. The authors systematically review theories from communication, psychology, and economics—organized into content-based, source-related, and network-level…
Psychology & interventionsDetection methods
Measuring and Reducing Misinformation SusceptibilityJon Roozenbeek presents two major contributions: (1) the Misinformation Susceptibility Test (MIST), a psychometrically validated 20-item measure of how well people distinguish true from false headlines, and (2) findings that video-based inoculation interventions teaching manipulation techniques significantly improve people's ability to…
Deepfakes & multimodalDetection methods
ReplayFactor: Replay-Aware Dual-Attribute Learning for Practical Speech Deepfake DetectionExisting speech deepfake detectors fail on physically replayed synthetic speech (played through a loudspeaker and re-recorded), a low-cost, practical threat to voice authentication systems. ReplayFactor addresses this by learning both source authenticity (human vs. generated) and replay presence (non-replayed vs. replayed) via SSL-based…
Deepfakes & multimodalDetection methods
GraphSeal: Graph-Augmented Speech Deepfake Detection via Temporal-Spectral Artifact PropagationGraphSeal proposes a dynamic temporal-spectral graph neural network for speech deepfake detection that outperforms fixed-topology baselines on generalization tasks. Unlike prior graph-based anti-spoofing methods that use feature-extraction-determined topologies, GraphSeal constructs utterance-specific edges from both temporal adjacency…
Deepfakes & multimodalDetection methods
CLRM: Cross-Layer Relation Modeling for Audio Deepfake DetectionAudio deepfake detection (ADD) distinguishes authentic speech from synthetic or manipulated speech. Existing methods either select informative layers from self-supervised learning (SSL) speech encoders or aggregate features across layers, but this discards useful variation in how different layers represent genuine vs. spoofed speech.
Deepfakes & multimodalDetection methods
UniGADD: Universal Generator-Agnostic Deepfake DetectorUniGADD proposes a two-stage supervised contrastive learning framework for detecting deepfakes from previously unseen generators. The method learns discriminative embeddings from known fake generators in stage one, then refines cluster structure via intra-cluster compactness constraints in stage two.
Deepfakes & multimodalDatasets & evaluation
DeepfakeGenome: Toward Next-Generation Deepfake AttributionDeepfakeGenome (DFG) is a large-scale benchmark for deepfake detection and attribution covering 100 facial forgery algorithms and 2M images — 4–100× larger than prior benchmarks. The paper proposes 4 evaluation protocols using novel retrieval-based attribution metrics (mAP, Recall) instead of reject-class classification, and conducts…
Deepfakes & multimodalDetection methods
VeriSpoof: Beyond Real or Fake with One Audio LLMVeriSpoof is an audio language model system that unifies multiple forensic tasks in a single neural framework: detecting whether a speech recording is synthetic, identifying which generator produced it, analyzing recording conditions, and verifying whether two recordings share a source. The system achieves state-of-the-art results on…
Detection methodsLLMs & generative AI
Propasafe-Hybrid: A Text-Based Hybrid Propaganda Detection ToolPropasafe-Hybrid is a sentence-level propaganda detection tool that combines an offline BERT classifier with LLM-based technique identification. The hybrid architecture reduces API token usage (filtering 80–87% of sentences locally) while delivering interpretable technique labels and explanations at the point of consumption.
Spread & networksDetection methods
Where Does Rhetorical Distortion Live? A Multi-Platform, Multi-Language Measurement of Influencer ContentRhetorical distortion—misleading framing that inflates significance, manufactures anxiety, or misrepresents novelty—is not uniformly distributed across platforms or account types. This work measures five linguistic dimensions of distortion (Significance Inflation, Anxiety Manufacture, Novelty Claims, Loaded Language, Temporal Distortion)…
LLMs & generative AIDetection methods
A Large Language Model (LLM) Approach to COVID-19 Fake News DetectionThis paper evaluates seven pre-trained transformer models and LLMs for binary classification of COVID-19 tweets as real or fake using the COVID-19 English Tweets dataset (6,420 tweets). GPT-2 achieves the best performance with 96.31% accuracy and 0.96 F1-score.
Platforms, policy & societyDetection methods
Benchmarking System One Models in Online ModerationThis paper evaluates System One Models (SOMs)—models that accept natural-language context but return structured, typed outputs—across five benchmarks for online content moderation. The key finding is that Jev, a specific SOM implementation, is competitive with specialized moderation systems on policy-grounded tasks and matches or exceeds…
Detection methodsDatasets & evaluation
Interpretive Variation and Discourse Structure in Detecting Enthymemes in Controversial Political DiscourseEnthymemes (arguments with unstated premises or conclusions) are a persuasion technique in social media that readers may or may not recognize. This paper develops a DeBERTa-based detector on 1,482 tweets about vaccination and immigration, showing that macro-F1 reaches ~0.67 on hard labels but plateaus due to genuine human disagreement.
Deepfakes & multimodalDetection methods
Swin Meets EfficientNet: Lightweight Architectures for GAN-Based Face ForensicsProposes lightweight Swin-Transformer and hybrid EfficientNet-Swin architectures for detecting GAN-generated synthetic faces under computational constraints. The hybrid EfficientNet-B0+Swin model achieves 99% accuracy and 99.44% recall on the 140K Real and Fake Faces dataset, combining convolutional local feature extraction with…
Detection methodsPsychology & interventions
A Multi-Branch Feature Fusion Approach for Health Misinformation Detection and PropagationThe paper presents a multi-branch fusion framework for detecting and characterizing health misinformation on social media, grounded in psychological theories (Elaboration Likelihood Model and Theory of Planned Behaviour). The model fuses transformer embeddings with rhetorical, stance, and theory-motivated linguistic features in a unified…
Detection methodsLLMs & generative AI
Lazy Grounding: Attacking Search Agents with Factual EvidenceRetrieval-augmented search agents fail to robustly distinguish between evidence for the current question and evidence for semantically nearby questions. By injecting factually correct documents supporting a neighboring question into the search corpus, agents often misapply this evidence to answer the original question, achieving accuracy…
LLMs & generative AIDetection methods
Build it, Break it, Repeat: Benchmarking and improving LLM-manipulated disinformation detection in social media postsThis paper presents BiBiR (Build-it-Break-it-Repeat), an iterative adversarial benchmarking framework for evaluating machine-generated text detectors under adversarial conditions relevant to social media. Two new datasets enable fine-tuning detectors and testing adversarial robustness.
Spread & networksDetection methods
Network Information Enhances Unreliable News Domain DetectionContent-based detection of unreliable news is increasingly difficult as low-reliability sources mimic credible journalism. The authors show that network structure—how domains co-occur in social networks—can improve news-domain reliability classification.
Deepfakes & multimodalLLMs & generative AI
The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News DetectionThis comprehensive survey systematically reviews the application of Large Vision-Language Models (LVLMs) to multimodal fake news detection (MFND). It proposes a novel three-branch taxonomy categorizing current approaches into parameter-frozen, parameter-tuning, and reasoning paradigms, each addressing different resource constraints and…
Spread & networksDetection methods
Spectral Analysis of Fake News PropagationThe paper develops a spectral view of fake news propagation by connecting graph spectra with structural properties of propagation trees. The authors derive spectral bounds capturing five key aspects of cascade structure—branching capacity, cascade scale, structural cohesion, propagation span, and diffusion dynamics—and use them as graph…
LLMs & generative AIDatasets & evaluation
'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic AssumptionsThe paper systematically analyzes the fragmented landscape of AI-generated text detection (AITD) task definitions in the literature and proposes a unified framework distinguishing five core notions with implicit assumptions about what constitutes "AI text." The authors introduce AITDNA, a novel dataset of 350+ texts from real human-AI…
Deepfakes & multimodalDetection methods
Agentic Mixed-Source Multi-Modal Misinformation Detection with Adaptive Test-Time ScalingVision-language models struggle with mixed-source multi-modal misinformation, where false information combines manipulated images with misleading text or text-image mismatches. This paper proposes AgentM³D, a hierarchical multi-agent framework that decomposes detection into modality-specific agents (textual, visual, cross-modal…
Detection methodsDeepfakes & multimodal
What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News PreviewsNews previews (headline + image) on social media can mislead readers through selective omission of critical context present in the full article. This paper introduces MM-MISLEADING, a benchmark of 6,000 multimodal news previews annotated for misleading omissions, and proposes OMGUARD, a framework combining interpretation-aware…
LLMs & generative AIDetection methods
Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital EcosystemsThis paper updates the state of generative AI and misinformation research since 2024, documenting the shift toward industrialized deception through improved LLMs and multimodal systems. The authors present two methodological tools—JudgeGPT (for measuring human perception of AI-generated content) and RogueGPT (for controlled stimulus…
Datasets & evaluationLLMs & generative AI
MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation DetectionMALINT is the first human-annotated English corpus pairing 1,600 news articles with annotations of both credibility and the malicious intentions behind disinformation. The paper evaluates 12 language models on intent classification and proposes Intent-Based Inoculation (IBI), an intent-augmented reasoning approach that improves zero-shot…
LLMs & generative AIDetection methods
Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News DetectionLLMs can generate convincing fake news, but malicious prompts induce consistent statistical divergences in token-level reconstruction probabilities. The authors propose LIFE (Linguistic Fingerprints Extraction), which identifies key fragments where real and fake news differ most in their probability signature, achieving 93.7% accuracy on…
Deepfakes & multimodalDetection methods
Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language ModelsThis paper introduces DECEPTIONDECODED, a benchmark of 12,000 image-caption pairs for evaluating multimodal misinformation detection with focus on creator intent. The authors find that state-of-the-art vision-language models struggle with intent reasoning and rely on superficial cues, motivating a framework for intent-aware multimodal…
Datasets & evaluationDeepfakes & multimodal
From Manipulation to Mistrust: Explaining Diverse Micro-Video Misinformation for Robust Debunking in the WildThe paper introduces WildFakeBench, a benchmark of 10,000+ real-world micro-videos spanning diverse misinformation types (manipulation, bias, AI-generated content, out-of-context) from six social platforms. The authors propose FakeAgent, a multi-agent reasoning framework combining neural networks and large language models, which achieves…
Psychology & interventionsDetection methods
Why People Believe Misinformation — and What We Can Do About ItJon Roozenbeek synthesizes eight years of misinformation research, examining why people believe false information and what interventions actually work. Grounding the analysis in the historical tobacco disinformation playbook, he argues that misinformation susceptibility stems not primarily from poor reasoning, but from media consumption…
Deepfakes & multimodalLLMs & generative AI
Synergizing LLMs with Global Label Propagation for Multimodal Fake News DetectionThis paper proposes Global Label Propagation Network with LLM-based Pseudo Labeling (GLPN-LLM), a framework for multimodal fake news detection that integrates LLM-generated pseudo labels with label propagation via graph neural networks. A mask-based global label propagation mechanism prevents label leakage during training while enabling…
Detection methodsDatasets & evaluation
A Decision-Based Heterogeneous Graph Attention Network for Multi-Class Fake News DetectionProposes DHGAT (Decision-based Heterogeneous Graph Attention Network), a semi-supervised method for six-class fake news classification on the LIAR dataset. Unlike standard GNNs that apply uniform neighborhood aggregation, DHGAT models news as heterogeneous graphs with multiple edge types derived from speaker profiles and enables each…
Detection methodsDatasets & evaluation
Neighborhood-Order Learning Graph Attention Network for Fake News DetectionProposes NOL-GAT (Neighborhood-Order Learning Graph Attention Network), a GNN architecture that allows each node to adaptively select its optimal hop-neighborhood at each layer, bypassing the fixed L-hop limitation of conventional message-passing GNNs. Evaluated on five fake news datasets, NOL-GAT outperforms baseline GNN methods,…
LLMs & generative AIDetection methods
E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification via MLLMsE-FreeM2 is a training-free, retrieval-based multimodal system for out-of-context (OOC) misinformation detection that leverages pretrained vision-language models (MLLMs) and two-stage chain-of-thought reasoning. By dynamically retrieving cross-modal evidence and ranking via visual similarity, the method achieves 90.0% overall accuracy on…
Datasets & evaluationDetection methods
A Guide to Misinformation Detection Data and EvaluationThis work surveys the largest collection of misinformation detection datasets to date (75 datasets from prior literature) and evaluates dataset quality across multiple dimensions. The authors assess whether datasets enable reliable veracity evaluation by analyzing keyword correlations, temporal correlations, and feasibility of claim…
Detection methodsLLMs & generative AI
Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News DetectionExisting fake news detectors rely on surface-level semantic patterns (emotional language, stylistic cues) and fail when writing styles shift. This paper proposes InSIDE, which combines semantic and intent signals via heterogeneous graphs: intent models the purpose behind an article (e.g., promoting a product), while semantics capture…
Deepfakes & multimodalLLMs & generative AI
Debunk and Infer: Multimodal Fake News Detection via Diffusion-Generated Evidence and LLM ReasoningThis paper introduces DIFND, a framework for multimodal fake news detection that generates refuting/authenticating evidence via diffusion models and performs reasoning over multimodal content using large language models (LLMs). The approach uses a chain-of-debunk strategy where multiple MLLMs act as collaborative agents to assess news…
LLMs & generative AIDetection methods
Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future DirectionsThis survey synthesizes recent advances in applying large language models to fake news detection on social media. The review covers four major LLM-based frameworks (MiLk-FD, FND-LLM, DAFND, SheepDog), identifies critical challenges around multimodal integration, adversarial robustness, scalability, and interpretability, and proposes…
LLMs & generative AIDetection methods
SINcon: Mitigate LLM-Generated Malicious Message Injection Attack for Rumor DetectionMessage Propagation Tree (MPT) based rumor detection models are vulnerable to LLM-generated malicious message injection attacks that alter tree topology and semantics. The paper proposes SINcon, a contrastive learning defense that regularizes models to assign similar predictive influence to nodes of different importance levels,…
Detection methodsLLMs & generative AI
Is Less Really More? Fake News Detection with Limited InformationSLIM (Systematically-selected Limited Information) replaces full news article text with strategically chosen subsets — extracted keywords, POS/NER sequence tags, or textual metadata — to address the computational cost and data-sparsity limitations of full-text fake news detection. Information-theoretic measures (normalized Shannon…
Detection methodsLLMs & generative AI
Propasafe: A BERT-based offline tool for propaganda detectionOriginal privacy-focused, offline browser extension using fine-tuned BERT on the Propaganda Techniques Corpus for sentence-level propaganda classification. No online inference required; binary classification (propaganda or non-propaganda).
Psychology & interventionsDetection methods
Fact or Fake? The Psychology of MisinformationEcker presents an overview of misinformation psychology, covering definition, sources, behavioral impacts, and evidence-based interventions. He demonstrates that misinformation influences reasoning even after people receive corrections (the continued influence effect), and that corrections are only partially effective.
Psychology & interventionsDetection methods
The Science of MisinformationVan Bavel synthesizes research on misinformation psychology, spread mechanisms, and evidence-based interventions. Misinformation is a persistent global risk (World Economic Forum priority).
Deepfakes & multimodalDetection methods
Exposing the Deception: Uncovering More Forgery Clues for Deepfake DetectionThis paper proposes a framework for deepfake detection that extracts both local and global forgery clues by decomposing facial features into disentangled local representations. Using mutual information theory to ensure comprehensive yet non-overlapping feature extraction, the method achieves state-of-the-art results on five benchmark…
LLMs & generative AIDetection methods
TELLER: A Trustworthy Framework For Explainable, Generalizable and Controllable Fake News DetectionTELLER proposes a dual-system framework for fake news detection that combines LLM-driven cognition (decomposing content into yes/no questions) with a neural-symbolic decision system (learning interpretable rules). The approach prioritizes explainability, generalizability, and human controllability, achieving 76% accuracy on GossipCop and…
LLMs & generative AIDatasets & evaluation
Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of TechniquesThis comprehensive survey analyzes the integration of Large Language Models (LLMs) with Graph Representation Learning (GRL), breaking down the design space into two primary components—knowledge extractors and organizers—plus operation techniques for integration and training. The work provides a novel taxonomy for understanding how LLMs…
LLMs & generative AIDetection methods
RAIDAR: Generative AI Detection via RewritingThis paper introduces Raidar, a method to detect machine-generated text by measuring how much LLMs modify text when asked to rewrite it. The key observation is that LLMs tend to preserve AI-generated text and modify human-written text more when rewriting.
LLMs & generative AIDetection methods
DELL: Generating Reactions and Explanations for LLM-Based Misinformation DetectionLarge language models struggle with factuality and hallucinations in misinformation detection. DELL proposes a three-stage pipeline where LLMs generate synthetic user reactions from diverse perspectives, create explanations for proxy tasks (sentiment, framing, propaganda tactics), and merge predictions from task-specific experts.
LLMs & generative AIDetection methods
Can LLM-Generated Misinformation Be Detected?LLM-generated misinformation (from ChatGPT, Llama, Vicuna) is detectably harder for both humans and automated detectors to identify than human-written misinformation with identical semantics. The paper builds a taxonomy of LLM-generated misinformation across types, domains, and generation methods, then empirically demonstrates that…
Psychology & interventionsDetection methods
Prebunking Misinformation on Social MediaJon Roozenbeek presents the case for prebunking—teaching people to recognize manipulation tactics before encountering false claims—rather than relying on reactive debunking. Drawing on inoculation theory from the 1960s, he argues that manipulation techniques recur across misinformation campaigns: emotional exploitation, false…
LLMs & generative AIDetection methods
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-ReflectionThis paper introduces SELF-RAG (Self-Reflective Retrieval-Augmented Generation), a framework that teaches language models to adaptively decide when to retrieve external passages, generate factually grounded responses, and critique their own outputs. Using special "reflection tokens," the model learns to self-evaluate at inference time…
LLMs & generative AIDatasets & evaluation
The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and PreventionLinkedIn's lack of verification enables fake accounts that harm legitimate users and the platform's integrity. This work introduces a dataset of 3600 profiles (legitimate, manually created fake, and ChatGPT-generated) and proposes SSTE, a method that detects fake profiles immediately after registration using only profile text.
LLMs & generative AIDetection methods
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and ExplainabilityA comprehensive survey of trustworthy Graph Neural Networks covering four critical dimensions: privacy attacks and defenses against membership/property inference and model extraction; robustness against adversarial perturbations; fairness mechanisms to prevent discrimination in graph-based predictions; and explainability methods to…
LLMs & generative AIDetection methods
Adapting Fake News Detection to the Era of Large Language ModelsAs large language models proliferate and machine-generated text becomes prevalent, traditional fake news detectors trained exclusively on human-written content are increasingly unreliable. This paper evaluates fake news detectors across three hypothetical eras—human-written dominance, mixed human-machine content, and machine-generated…
LLMs & generative AIDetection methods
The Science of Detecting LLM-Generated TextsThis survey comprehensively reviews techniques for detecting LLM-generated text, a critical problem given concerns about misinformation, academic dishonesty, and unauthorized use. The authors categorize detection approaches into black-box (API-level access) and white-box (full model access) methods, discuss watermarking strategies,…
Spread & networksDetection methods
The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A SurveyThis survey systematically examines distributed systems for scalable GNN training, bridging graph processing systems (Pregel, PowerGraph, GraphLab) and DNN training systems (TensorFlow, PyTorch) to show how techniques from both domains inform GNN system design. The paper categorizes methods across partitioning, sampling, communication,…
LLMs & generative AIPlatforms, policy & society
Jailbroken: How Does LLM Safety Training Fail?This paper investigates why large language models trained for safety remain vulnerable to jailbreak attacks. Through analysis of models including GPT-4 and Claude v1.3, the authors identify two key failure modes: competing objectives (where safety constraints conflict with other capabilities) and mismatched generalization (where safety…
LLMs & generative AIDetection methods
A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future DirectionsAs LLMs become ubiquitous, detecting machine-generated text is critical for mitigating risks of misinformation, academic dishonesty, and misuse. This survey comprehensively reviews detection strategies—watermarking techniques, statistical methods, neural-based detectors, and human-assisted approaches—along with datasets, evaluation…
LLMs & generative AIDetection methods
Combating Misinformation in the Age of LLMs: Opportunities and ChallengesThis survey systematically reviews how large language models (LLMs) can both combat misinformation and generate it at scale. LLMs offer opportunities for detection, intervention, and attribution through their world knowledge and reasoning abilities, but also pose challenges as they can create deceptive content intentionally or through…
LLMs & generative AIDetection methods
A Watermark for Large Language ModelsProposes a watermarking scheme for large language models that embeds imperceptible signals into generated text, enabling detection of machine-generated content without access to model parameters or APIs. The watermark works by promoting a randomized set of "green" tokens during decoding and can be detected using a statistical z-test; the…
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…