Papers with fake news detection
Entity-Aware Dual Co-Attention Network for Fake News Detection (2023.findings-eacl)
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| Challenge: | Existing models for fake news detection are limited in their ability to detect it from different aspects. |
| Approach: | They propose a Dual Co-Attention Network (Dual-CAN) for fake news detection that takes news content, social media replies, and external knowledge into consideration. |
| Outcome: | The proposed model outperforms existing models in two benchmark datasets. |
VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning (2024.acl-demos)
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Cheng Niu, Yang Guan, Yuanhao Wu, Juno Zhu, Juntong Song, Randy Zhong, Kaihua Zhu, Siliang Xu, Shizhe Diao, Tong Zhang
| Challenge: | generative artificial intelligence has exacerbated the challenge of distinguishing genuine news from fabricated stories. |
| Approach: | They propose a retrieval-augmented system that extracts the core facts from a given piece of news and conducts an internet-wide search to identify corroborating or conflicting reports. |
| Outcome: | The proposed system has demonstrated state-of-the-art accuracy in the realm of fake news detection. |
On Fake News Detection with LLM Enhanced Semantics Mining (2024.emnlp-main)
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| Challenge: | Existing methods for detecting fake news use only news embeddings to capture the lexical semantics between tokens. |
| Approach: | They propose a topic-based model with prompts to extract news embeddings from LLMs and a generalized page-rank model to extract local and global semantics. |
| Outcome: | The proposed model shows superior performance on five benchmark datasets over seven baseline methods. |
Cross-lingual Evidence Improves Monolingual Fake News Detection (2021.acl-srw)
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| Challenge: | Existing methods focused on one language and do not use multilingual information. |
| Approach: | They propose a new technique based on cross-lingual evidence that can be used for fake news detection . they compared their proposed technique with strong baselines on two datasets of general-topic news . |
| Outcome: | The proposed technique improves existing methods and can be used on real and fake news datasets. |
Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge (2021.acl-long)
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| Challenge: | Existing methods for fake news detection rely on linguistic and semantic features from news content and do not exploit external knowledge. |
| Approach: | They propose a graph neural model which compares news to knowledge base through entities for fake news detection. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods on two benchmark datasets. |
Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection (2021.eacl-main)
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| Challenge: | Existing methods to fact-check information focus on word-level attention or evidence-level focus, which may result in suboptimal performance. |
| Approach: | They propose a Hierarchical Multi-head Attentive Network to fact-check textual claims using word-level attention and document-level focus. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two real-word datasets. Improvements over baselines are from 6% to 18%. |
Multi-Source Multi-Class Fake News Detection (C18-1)
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| Challenge: | detecting fake news is challenging especially in the era of social media, as it is written intentionally to mislead readers. |
| Approach: | They propose a framework to combine information from multiple sources and discriminate between different degrees of fakeness. |
| Outcome: | The proposed framework can detect fake news with different degrees of fakeness . it integrates information from multiple sources and discriminates between them . |
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection (2021.acl-long)
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Yi Fung, Christopher Thomas, Revanth Gangi Reddy, Sandeep Polisetty, Heng Ji, Shih-Fu Chang, Kathleen McKeown, Mohit Bansal, Avi Sil
| Challenge: | a novel approach to detect fake news is needed due to training data scarcity . current methods focus on document-level fake news detection using lexical features and semantic embeddings . |
| Approach: | They propose a novel benchmark for fake news detection at the knowledge element level . they propose synthesis method which manipulates knowledge elements to generate noisy training data . |
| Outcome: | The proposed method outperforms the state-of-the-art in detecting misinformation . it yields fine-grained explanations and outperformed the current methods . |
Fake News Detection using Deep Markov Random Fields (N19-1)
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| Challenge: | Existing deep-learning-based methods ignore the correlations among news articles and only consider each article individually. |
| Approach: | They propose a graph-theoretic method that inherits the power of deep learning while utilizing the correlations among the articles. |
| Outcome: | The proposed model improves on state-of-the-art models on well-known datasets. |
Have LLMs Reopened the Pandora’s Box of AI-Generated Fake News? (2025.naacl-long)
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Xinyu Wang, Wenbo Zhang, Sai Koneru, Hangzhi Guo, Bonam Mingole, S. Shyam Sundar, Sarah Rajtmajer, Amulya Yadav
| Challenge: | Large language models (LLMs) are increasingly being used by fake news creators to generate deceptive and persuasive content at scale. |
| Approach: | They propose to use large language models to generate fake news at scale and to assess the ability of human annotators and AI models to detect it. |
| Outcome: | The results show that LLMs are 68% more effective at detecting real news than humans, compared to humans and AI models for fake news detection. |
Connecting the Dots Between Fact Verification and Fake News Detection (2020.coling-main)
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| Challenge: | Existing methods for detecting fake news rely heavily on supervised learning on a large scale dataset with news articles labeled as fake or real by human experts. |
| Approach: | They propose a simple yet effective approach to connect the dots between fact verification and fake news detection by using a text summarization model pre-trained on news corpora to summarize the long news article into a short claim. |
| Outcome: | The proposed approach enables zero-shot fake news detection, alleviating the need for large scale training data to train fake news detector models. |
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation (2021.findings-emnlp)
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| Challenge: | Recent advances in generative language models have enabled machines to generate realistic texts. |
| Approach: | They propose a benchmark environment to test the 'Turing Test' problem for neural text generation methods. |
| Outcome: | The proposed benchmark environment is based on 200K human- or machine-generated samples across 20 labels Human, GPT-1, GTP-2_small, GTT-2_medium, GPG-2_large, GGT-2_PyTorch, GGP-3, GROVER_base, griover_large and GRover_mega. |
Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing (2020.lrec-1)
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| Challenge: | a lack of publicly available news bias datasets has hindered efforts to detect subtle biases in news articles. |
| Approach: | They propose a news bias dataset which contains sentences with bias labels . they propose to use the dataset to develop and evaluate methods for detecting news bias . |
| Outcome: | The proposed dataset can be used for analyzing news bias and for developing and evaluating methods for news bias detection. |
Multimodal Fusion with Co-Attention Networks for Fake News Detection (2021.findings-acl)
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| Challenge: | Existing methods to detect fake news with textual and visual contents are ineffective because they concatenate unimodal features without considering inter-modality relations. |
| Approach: | They propose to fuse textual and visual features for fake news detection using multimodal co-attention networks to learn inter-dependencies between multimodal features. |
| Outcome: | Extensive experiments on two realworld datasets show that the proposed network outperforms state-of-the-art methods and learns inter-dependencies among multimodal features. |
Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection (2022.coling-1)
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| Challenge: | Existing methods to detect fake news neglect a broader propagation uncertainty issue . Existing studies leverage the user interactions in a social media conversation thread to detect false news. |
| Approach: | They propose a dual graph-based model for improving fake news detection . they propose to explore latent interactions in the actual propagation . |
| Outcome: | The proposed model improves on two real-world datasets showing that it is superior to existing models. |
A Unified Propagation Forest-based Framework for Fake News Detection (2022.coling-1)
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| Challenge: | Recent studies on fake news detection have focused on textual news material, but there is a lack of authoritative regulators. |
| Approach: | They propose a framework to explore latent correlations between propagation trees and a root-induced training strategy to encourage representations of propagation tree to be closer to their prototypical root nodes. |
| Outcome: | The proposed framework explores latent correlations between propagation trees to improve fake news detection. |
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer (2022.coling-1)
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| Challenge: | Social media spreads both real news and fake news in various domains including politics, health, entertainment, etc. |
| Approach: | They propose a Domain- and Instance-level Transfer Framework for Fake News Detection which could improve the performance of specific target domains. |
| Outcome: | The proposed framework improves performance of target domains by hurting other domains, resulting in unsatisfactory performance in the target domain. |
FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adapter (2025.findings-emnlp)
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| Challenge: | Existing methods for detecting fake news videos fall short due to lack of knowledge to verify the news is real or not. |
| Approach: | They propose a VLM-based framework for detecting fake news on short video platforms . they design four experts tailored to handle each scenario and integrate them into VLM . |
| Outcome: | The proposed framework outperforms current state-of-the-art models on two benchmark datasets. |
KAPALM: Knowledge grAPh enhAnced Language Models for Fake News Detection (2023.findings-emnlp)
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| Challenge: | Existing methods of fake news detection focus on news entity information and ignore structured knowledge among news entities. |
| Approach: | They propose a model that fuses coarse- and fine-grained representations of entity knowledge from Knowledge Graphs (KGs) they identify entities in news content and link them to entities in KGs. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets and is competitive in the few-shot scenario. |
Claim veracity assessment for explainable fake news detection (2025.coling-main)
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| Challenge: | Recent approaches to fake news detection focus on textual features without external facts, which may lead to a misrepresentation of the truth. |
| Approach: | They propose a new fake news detection method that predicts the truth or false-hood of a claim based on relevant factual evidence or LLM’s inference mechanisms. |
| Outcome: | The proposed method produces the final synthesized prediction, along with well-founded facts or reasoning. |
Graph-based Fake News Detection using a Summarization Technique (2021.eacl-main)
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| Challenge: | Existing methods to detect fake news using external information and internal information are difficult to identify external information in all documents. |
| Approach: | They propose a graph-based fake news detection method that uses only the document internal information to represent the relationship between all sentences using a diagram and the reflection rate of contextual information among sentences is computed by using an attention mechanism. |
| Outcome: | The proposed method achieves high accuracy, 91.04%, that is 8.85%p better than the previous method. |
Automatic Detection of Fake News (C18-1)
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| Challenge: | a growing number of fake news detection tools are needed to identify trustworthy news sources. |
| Approach: | They propose to use two novel datasets to automate the identification of fake news . they propose learning experiments to build accurate fake news detectors . |
| Outcome: | The proposed algorithms achieve accuracies of up to 76% and compare them with other tools . the proposed algorithms are based on satirical news sources and fact-checking websites . |
Data Augmentation using Machine Translation for Fake News Detection in the Urdu Language (2020.lrec-1)
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| Challenge: | supervised machine learning requires substantial amount of annotated data. |
| Approach: | They propose to use machine translation to augment annotated corpora for fake news detection in Urdu . they train a fake news classifier on an annotation dataset originally in Uru . |
| Outcome: | The proposed method fails to improve fake news detection in Urdu at the current state of machine translation quality. |
Event-Radar: Event-driven Multi-View Learning for Multimodal Fake News Detection (2024.acl-long)
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| Challenge: | Existing methods for detecting multimedia fake news have demonstrated excellent results . however, addressing event-level inconsistency and learning from poor-quality news remains a challenge . |
| Approach: | They propose an Event-diven fake news detection framework that integrates visual manipulation, textual emotion and multimodal inconsistency at event-level for fake news identification. |
| Outcome: | The proposed framework performs well on three large-scale fake news detection benchmarks. |
Localization of Fake News Detection via Multitask Transfer Learning (2020.lrec-1)
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| Challenge: | Existing methods for detecting fake news require large labeled datasets and expert-curated corpora, which low-resource languages may not have. |
| Approach: | They construct a benchmark dataset for fake news detection in Filipino using curated corpora and transfer learning techniques. |
| Outcome: | The proposed method can achieve 91% accuracy on a fake news dataset, reducing error by 14% compared to established baselines. |
Reliability Estimation of News Media Sources: Birds of a Feather Flock Together (2024.naacl-long)
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| Challenge: | Recent research has shown that predicting sources’ reliability is an important first-prior step in addressing additional challenges such as fake news detection and fact-checking. |
| Approach: | They propose a method that leverages reinforcement learning strategies to estimate the reliability degree of news sources based on how all the news media sources interact with each other on the Web. |
| Outcome: | The proposed method can predict reliability labels on a large news media reliability dataset. |
Topology Imbalance and Relation Inauthenticity Aware Hierarchical Graph Attention Networks for Fake News Detection (2022.coling-1)
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| Challenge: | Existing methods to detect fake news focus on mining lexical and syntactic features. |
| Approach: | They propose a topology imbalance and Relation inauthenticity aware Hierarchical Graph Attention Networks to identify fake news on social media. |
| Outcome: | The proposed method outperforms state-of-the-art methods on real-world datasets. |
CLFD: A Novel Vectorization Technique and Its Application in Fake News Detection (2020.lrec-1)
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| Challenge: | Existing work on fake news detection is limited due to the complex nature of the news . |
| Approach: | They propose a statistical approach for the generation of feature vectors to describe a document . they use class label frequency distance to boost machine learning methods . |
| Outcome: | The proposed method outperforms deep learning methods in large datasets while outperforming traditional methods. |
TripleFact: Defending Data Contamination in the Evaluation of LLM-driven Fake News Detection (2025.acl-long)
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| Challenge: | Existing evaluation paradigms for fake news detection are based on static datasets and closed-world assumptions that are inadvertently memorized during pre-training. |
| Approach: | They propose a framework to mitigate BDC risk while prioritizing real-world applicability by integrating three components to assess robustness against human-crafted misinformation. |
| Outcome: | The proposed framework mitigates BDC risk while prioritizing real-world applicability. |
Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection (D19-1)
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| Challenge: | Existing methods for detecting fake news use shared features as complementarity features without selection. |
| Approach: | They propose a sifted multi-task learning method with a selected sharing layer for fake news detection. |
| Outcome: | The proposed method boosts the F1-score by more than 0.87%, 1.31% on two public and widely used competition datasets. |
Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users based on Weakly Supervised Learning (2020.coling-main)
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| Challenge: | Existing models for fake news detection are often insufficient or lacking in features . a novel structure-aware multi-head attention network can detect fake news in 4 hours . |
| Approach: | They propose a structure-aware multi-head attention network to detect fake news in mass news . they use credibility of publishers and users as prior weakly supervised information . |
| Outcome: | The proposed model can detect fake news in 4 hours with an accuracy of over 91% . the proposed model is faster than the state-of-the-art models . |
Unveiling Fake News with Adversarial Arguments Generated by Multimodal Large Language Models (2025.coling-main)
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| Challenge: | Existing methods for detecting fake news rely on neural networks to learn latent feature representations with limited real-world understanding. |
| Approach: | They propose a method that leverages Multimodal Large Language Models for fake news detection that introduces adversarial reasoning through debates from opposing perspectives. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on four fake news detection datasets. |
Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration (2025.coling-main)
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| Challenge: | Rhetorical figures are used to convey subtle, implicit meanings or to emphasize statements. |
| Approach: | They propose a web application that facilitates the identification and annotation of German rhetorical figures. |
| Outcome: | The proposed application improves the user experience with Retrieval Augmented Generation (RAG). |
Exploring the Usability of Persuasion Techniques for Downstream Misinformation-related Classification Tasks (2024.lrec-main)
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| Challenge: | systematically explore the predictive power of features derived from Persuasion Techniques detected in texts for different tasks of interest for media analysis. |
| Approach: | They propose a set of meaningful features aimed at capturing persuasiveness of a text . they also assess the discriminatory power of these features in different text classification tasks . |
| Outcome: | The proposed features can be applied to detecting mis/disinformation, fake news, propaganda, partisan news and conspiracy theories. |
Structure-aware Propagation Generation with Large Language Models for Fake News Detection (2025.findings-emnlp)
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| Challenge: | propagation-based methods for fake news detection often lack structural data . authors propose a structure-aware synthetic propagation enhanced detection framework . |
| Approach: | They propose a structure-aware synthetic propagation enhanced detection framework to capture real-world propagation. |
| Outcome: | The proposed framework captures structural dynamics from real propagation, while ignoring structural patterns. |
A Survey on Natural Language Processing for Fake News Detection (2020.lrec-1)
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| Challenge: | Automated fake news detection is a critical but challenging problem in NLP . social media has accelerated the spread of fake news, threatening public safety . |
| Approach: | They describe the challenges involved in fake news detection and describe related tasks . they outline promising research directions and highlight the difference between fake news and related tasks. |
| Outcome: | The proposed models are more fine-grained, detailed, fair, and practical. |
French Tweet Corpus for Automatic Stance Detection (2020.lrec-1)
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| Challenge: | a new corpus of tweets is being developed for automatic stance detection of fake news . the task involves determining the attitude expressed in a text toward a target . this is a difficult task to overcome as discussions about fake news are controversial . |
| Approach: | They propose to build a human-annotated corpus for automatic stance detection of tweets in french . they propose to use four classes broadly adopted by the community for annotation . |
| Outcome: | The proposed corpus is the first freely available stance annotated tweet corpus in the french language. |
Vision-Language Models Struggle to Align Entities across Modalities (2025.findings-acl)
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| Challenge: | Several real-world applications require the ability to perform cross-modal entity linking . cross-functional entity linking is a skill needed for multimodal code generation and scene understanding . |
| Approach: | They propose a task and benchmark to evaluate cross-modal entity linking performance . they use visual scenes aligned with their textual representations to evaluate performance a question-answering task . |
| Outcome: | The proposed task and benchmark aims to improve cross-modal entity linking performance . it evaluates state-of-the-art vision-language models and humans on the task . |
ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are limited by knowledge cutoff and can generate factual hallucinations when handling time-sensitive news. |
| Approach: | They propose a two-stage zero-shot fake news detection framework that uses a hierarchical salience and saliency-calibrated minimum margin of relevance algorithm to extract core entities accurately. |
| Outcome: | The proposed framework outperforms existing zero-shot baselines and even most few-shot methods on two public datasets. |
Revealing Hidden Mechanisms of Cross-Country Content Moderation with Natural Language Processing (2025.findings-acl)
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| Challenge: | Existing knowledge on how and why NLP methods make content moderation decisions is limited . authors examine how and when to use LLMs in content modeation . |
| Approach: | They use Shapley values and LLM-guided explanations to reverse-engineer content moderation decisions across countries. |
| Outcome: | The proposed methods show that they reverse-engineer content moderation decisions across countries and over time. |
DCR: Quantifying Data Contamination in LLMs Evaluation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) memorize evaluation data during training, inflating performance metrics and undermining genuine generalization assessment. |
| Approach: | They propose a framework to detect and quantify benchmark data contamination (BDC) by synthesizing contamination scores via a fuzzy inference system. |
| Outcome: | The proposed framework detects and quantifies BDC risk across semantic, informational, data, and label levels. |
Dialectical Structured Reasoning for Explainable Multimodal Fake News Detection (2026.findings-acl)
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Ruichao Yang, Yufan Bian, Wei Gao, Bo-Wen Zhang, Jing Ma, Hongzhan Lin, Ziyang Luo, Xiaobin Zhu, Xu-Cheng Yin
| Challenge: | Existing fake news detection models are opaque and lack deductive transparency . a framework for dialectical structured reasoning is proposed to address this limitation . |
| Approach: | They propose a framework that model fake news detection as an explicit dialectical process over multimodal social context. |
| Outcome: | The proposed framework achieves state-of-the-art while producing transparent explanations that mirror human reasoning process. |