A Hybrid Attention Framework for Fake News Detection with Large Language Models

• URL: http://arxiv.org/abs/2501.11967v1
• Date: Tue, 21 Jan 2025 08:26:20 GMT
• Title: A Hybrid Attention Framework for Fake News Detection with Large Language Models
• Authors: Xiaochuan Xu, Peiyang Yu, Zeqiu Xu, Jiani Wang,
• Abstract summary: We propose a novel framework to identify and classify fake news by integrating textual statistical features and deep semantic features. Our approach utilizes the contextual understanding capability of the large language model for text analysis. Our model significantly outperforms existing methods, with a 1.5% improvement in F1 score.
• Score: 0.0
• License:
• Abstract: With the rapid growth of online information, the spread of fake news has become a serious social challenge. In this study, we propose a novel detection framework based on Large Language Models (LLMs) to identify and classify fake news by integrating textual statistical features and deep semantic features. Our approach utilizes the contextual understanding capability of the large language model for text analysis and introduces a hybrid attention mechanism to focus on feature combinations that are particularly important for fake news identification. Extensive experiments on the WELFake news dataset show that our model significantly outperforms existing methods, with a 1.5\% improvement in F1 score. In addition, we assess the interpretability of the model through attention heat maps and SHAP values, providing actionable insights for content review strategies. Our framework provides a scalable and efficient solution to deal with the spread of fake news and helps build a more reliable online information ecosystem.

Related papers

• A Self-Learning Multimodal Approach for Fake News Detection [35.98977478616019]
  We introduce a self-learning multimodal model for fake news classification. The model leverages contrastive learning, a robust method for feature extraction that operates without requiring labeled data. Our experimental results on a public dataset demonstrate that the proposed model outperforms several state-of-the-art classification approaches.
  arXiv  Detail & Related papers  (2024-12-08T07:41:44Z)
• Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI [44.21078435758592]
  Misinformation can spread quickly due to the ease of creating and disseminating content. Traditional approaches to fake news detection often rely solely on content-based features. We propose a comprehensive approach that integrates social context-based features with news content features.
  arXiv  Detail & Related papers  (2024-10-03T15:49:35Z)
• Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection [50.079690200471454]
  Few-Shot Fake News Detection (FS-FND) aims to distinguish inaccurate news from real ones in extremely low-resource scenarios. This task has garnered increased attention due to the widespread dissemination and harmful impact of fake news on social media. 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)
• Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection [50.07850264495737]
  "Prompt-and-Align" (P&A) is a novel prompt-based paradigm for few-shot fake news detection. We show that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.
  arXiv  Detail & Related papers  (2023-09-28T13:19:43Z)
• No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation [16.40196904371682]
  We propose a novel framework for fake news detection from perspectives of semantic, emotion and data enhancement. A dual deep interaction channel network of semantic and emotion is designed to obtain a more comprehensive and fine-grained news representation. Experiments show that the proposed approach outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-31T13:33:53Z)
• Multiverse: Multilingual Evidence for Fake News Detection [71.51905606492376]
  Multiverse is a new feature based on multilingual evidence that can be used for fake news detection. The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed.
  arXiv  Detail & Related papers  (2022-11-25T18:24:17Z)
• Interpretable Fake News Detection with Topic and Deep Variational Models [2.15242029196761]
  We focus on fake news detection using interpretable features and methods. We have developed a deep probabilistic model that integrates a dense representation of textual news. Our model achieves comparable performance to state-of-the-art competing models.
  arXiv  Detail & Related papers  (2022-09-04T05:31:00Z)
• Multimodal Fusion with BERT and Attention Mechanism for Fake News Detection [0.0]
  We present a novel method for detecting fake news by fusing multimodal features derived from textual and visual data. Experimental results showed that our approach performs better than the current state-of-the-art method on a public Twitter dataset by 3.1% accuracy.
  arXiv  Detail & Related papers  (2021-04-23T08:47:54Z)
• InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective [84.78604733927887]
  Large-scale language models such as BERT have achieved state-of-the-art performance across a wide range of NLP tasks. Recent studies show that such BERT-based models are vulnerable facing the threats of textual adversarial attacks. We propose InfoBERT, a novel learning framework for robust fine-tuning of pre-trained language models.
  arXiv  Detail & Related papers  (2020-10-05T20:49:26Z)
• PIN: A Novel Parallel Interactive Network for Spoken Language Understanding [68.53121591998483]
  In the existing RNN-based approaches, ID and SF tasks are often jointly modeled to utilize the correlation information between them. The experiments on two benchmark datasets, i.e., SNIPS and ATIS, demonstrate the effectiveness of our approach. More encouragingly, by using the feature embedding of the utterance generated by the pre-trained language model BERT, our method achieves the state-of-the-art among all comparison approaches.
  arXiv  Detail & Related papers  (2020-09-28T15:59:31Z)

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.