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Spread & networksPlatforms, policy & society
Fear Doesn't Go Viral, It Lingers: Spread, Affect, and Discourse of Food-Fear Content on TikTokFear-based content about food on TikTok reaches fewer people than non-fear nutrition videos but sustains engagement and discussion far longer. Fear videos trigger more negative, angry, and disgusted comments that persist for weeks; their discourse concentrates on distrust of food industry, contamination, and institutional skepticism.
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 & 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…
LLMs & generative AIFact-checking & verification
VEX-Bench: Benchmarking Verification Complexity of LLM-Generated MisinformationThis paper introduces VEX-Bench, a unified benchmark for evaluating how difficult it is to verify LLM-generated misinformation. The benchmark measures verification complexity across five dimensions: checkability (whether claims can be checked), harm significance, source credibility signals, impostor legitimacy, and verification cost.
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…
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…
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…
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…
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…
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…
LLMs & generative AIDatasets & evaluation
Beyond Speculation: Measuring the Growing Presence of LLM-Generated Texts in Multilingual DisinformationThis empirical study provides the first measurement of LLM-generated content in real-world disinformation datasets. Analyzing the MultiClaim dataset (fact-checked social media posts), the authors document a 99% increase in machine-generated content from 2021 to 2023 (0.93% to 1.85%), correlating with ChatGPT's November 2022 release.
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…
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…
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).