Explore papers
Filter by research area, year and venue. Every filter is in the address bar, so you can share a view as a link.
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…
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 & 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…
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,…
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…
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…
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…
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…
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…
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,…
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…
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 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).
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
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 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
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…
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
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…
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 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…
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 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,…