Towards Real-Time Fake News Detection under Evidence Scarcity

• URL: http://arxiv.org/abs/2510.11277v1
• Date: Mon, 13 Oct 2025 11:11:46 GMT
• Title: Towards Real-Time Fake News Detection under Evidence Scarcity
• Authors: Guangyu Wei, Ke Han, Yueming Lyu, Yu Luo, Yue Jiang, Caifeng Shan, Nicu Sebe,
• Abstract summary: We propose Evaluation-Aware Selection of Experts (EASE), a novel framework for real-time fake news detection.<n>EASE adapts its decision-making process according to the assessed sufficiency of available evidence.<n>We introduce RealTimeNews-25, a new benchmark for evaluating model generalization on emerging news with limited evidence.
• Score: 66.58597356379907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fake news detection becomes particularly challenging in real-time scenarios, where emerging events often lack sufficient supporting evidence. Existing approaches often rely heavily on external evidence and therefore struggle to generalize under evidence scarcity. To address this issue, we propose Evaluation-Aware Selection of Experts (EASE), a novel framework for real-time fake news detection that dynamically adapts its decision-making process according to the assessed sufficiency of available evidence. EASE introduces a sequential evaluation mechanism comprising three independent perspectives: (1) Evidence-based evaluation, which assesses evidence and incorporates it into decision-making only when the evidence is sufficiently supportive; (2) Reasoning-based evaluation, which leverages the world knowledge of large language models (LLMs) and applies them only when their reliability is adequately established; and (3) Sentiment-based fallback, which integrates sentiment cues when neither evidence nor reasoning is reliable. To enhance the accuracy of evaluation processes, EASE employs instruction tuning with pseudo labels to guide each evaluator in justifying its perspective-specific knowledge through interpretable reasoning. Furthermore, the expert modules integrate the evaluators' justified assessments with the news content to enable evaluation-aware decision-making, thereby enhancing overall detection accuracy. Moreover, we introduce RealTimeNews-25, a new benchmark comprising recent news for evaluating model generalization on emerging news with limited evidence. Extensive experiments demonstrate that EASE not only achieves state-of-the-art performance across multiple benchmarks, but also significantly improves generalization to real-time news. The code and dataset are available: https://github.com/wgyhhhh/EASE.

Related papers

• User-Centric Evidence Ranking for Attribution and Fact Verification [12.059713949823335]
  Evidence Ranking is a novel task that prioritizes presenting sufficient information as early as possible in a ranked list.<n>This minimizes user reading effort while still making all available evidence accessible for sequential verification.<n>This work provides a foundational step toward more interpretable, efficient, and user-aligned information verification systems.
  arXiv  Detail & Related papers  (2026-01-29T08:23:26Z)
• ExDR: Explanation-driven Dynamic Retrieval Enhancement for Multimodal Fake News Detection [23.87220484843729]
  multimodal fake news poses a serious societal threat.<n> Dynamic Retrieval-Augmented Generation provides a promising solution by triggering keyword-based retrieval.<n>We propose ExDR, an Explanation-driven Dynamic Retrieval-Augmented Generation framework for Multimodal Fake News Detection.
  arXiv  Detail & Related papers  (2026-01-22T10:10:06Z)
• Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metrics [89.1999907891494]
  We present WebDetective, a benchmark of hint-free multi-hop questions paired with a controlled Wikipedia sandbox.<n>Our evaluation of 25 state-of-the-art models reveals systematic weaknesses across all architectures.<n>We develop an agentic workflow, EvidenceLoop, that explicitly targets the challenges our benchmark identifies.
  arXiv  Detail & Related papers  (2025-10-01T07:59:03Z)
• Existing Large Language Model Unlearning Evaluations Are Inconclusive [105.55899615056573]
  We show that some evaluations introduce substantial new information into the model, potentially masking true unlearning performance.<n>We demonstrate that evaluation outcomes vary significantly across tasks, undermining the generalizability of current evaluation routines.<n>We propose two principles for future unlearning evaluations: minimal information injection and downstream task awareness.
  arXiv  Detail & Related papers  (2025-05-31T19:43:00Z)
• Towards Automated Situation Awareness: A RAG-Based Framework for Peacebuilding Reports [2.230742111425553]
  This paper introduces a dynamic Retrieval-Augmented Generation (RAG) system that autonomously generates situation awareness reports.<n>Our system constructs query-specific knowledge bases on demand, ensuring timely, relevant, and accurate insights.<n>The system is tested across multiple real-world scenarios, demonstrating its effectiveness in producing coherent, insightful, and actionable reports.
  arXiv  Detail & Related papers  (2025-05-14T16:36:30Z)
• Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checking [11.300523252168327]
  Evtextsuperscript2R combines the strengths of reference-based evaluation and verdict-level proxy scoring.<n>Evtextsuperscript2R consistently outperforms existing scoring approaches in accuracy and robustness.
  arXiv  Detail & Related papers  (2024-11-08T07:05:06Z)
• TrendFact: A Benchmark for Explainable Hotspot Perception in Fact-Checking with Natural Language Explanation [10.449165630417522]
  We introduce TrendFact, the first hotspot perception fact-checking benchmark.<n> TrendFact consists of 7,643 carefully curated samples sourced from trending platforms and professional fact-checking datasets.<n>We propose FactISR, which integrates dynamic evidence augmentation, evidence triangulation, and an iterative self-reflection mechanism.
  arXiv  Detail & Related papers  (2024-10-19T15:25:19Z)
• Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection [53.41813030290324]
  Few-Shot Fake News Detection (FS-FND) aims to distinguish inaccurate news from real ones in extremely low-resource scenarios.<n>This task has garnered increased attention due to the widespread dissemination and harmful impact of fake news on social media.<n>We propose a Dual-perspective Knowledge-guided Fake News Detection (DKFND) model, designed to enhance LLMs from both inside and outside perspectives.
  arXiv  Detail & Related papers  (2024-07-12T03:15:01Z)
• Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction [56.17020601803071]
  Recent research shows that pre-trained language models (PLMs) suffer from "prompt bias" in factual knowledge extraction. This paper aims to improve the reliability of existing benchmarks by thoroughly investigating and mitigating prompt bias.
  arXiv  Detail & Related papers  (2024-03-15T02:04:35Z)
• Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors [38.75533934195315]
  Large Language Models (LLMs) are known for their remarkable reasoning and generative capabilities. We introduce a novel, retrieval-augmented LLMs framework--the first of its kind to automatically and strategically extract key evidence from web sources for claim verification. Our framework ensures the acquisition of sufficient, relevant evidence, thereby enhancing performance.
  arXiv  Detail & Related papers  (2024-03-14T00:35:39Z)
• AttributionBench: How Hard is Automatic Attribution Evaluation? [19.872081697282002]
  We present AttributionBench, a comprehensive benchmark compiled from various existing attribution datasets. Our experiments show that even a fine-tuned GPT-3.5 only achieves around 80% macro-F1 under a binary classification formulation. A detailed analysis of more than 300 error cases indicates that a majority of failures stem from the model's inability to process nuanced information.
  arXiv  Detail & Related papers  (2024-02-23T04:23:33Z)
• DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection [55.70982767084996]
  A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. We present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions. DeepfakeBench contains 15 state-of-the-art detection methods, 9CL datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations.
  arXiv  Detail & Related papers  (2023-07-04T01:34:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.