Multi-view autoencoders for Fake News Detection

• URL: http://arxiv.org/abs/2504.08102v1
• Date: Thu, 10 Apr 2025 19:59:34 GMT
• Title: Multi-view autoencoders for Fake News Detection
• Authors: Ingryd V. S. T. Pereira, George D. C. Cavalcanti, Rafael M. O. Cruz,
• Abstract summary: This paper proposes using multi-view autoencoders to generate a joint feature representation for fake news detection.<n>Experiments on fake news datasets show a significant improvement in classification performance compared to individual views.
• Score: 5.863538874435322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given the volume and speed at which fake news spreads across social media, automatic fake news detection has become a highly important task. However, this task presents several challenges, including extracting textual features that contain relevant information about fake news. Research about fake news detection shows that no single feature extraction technique consistently outperforms the others across all scenarios. Nevertheless, different feature extraction techniques can provide complementary information about the textual data and enable a more comprehensive representation of the content. This paper proposes using multi-view autoencoders to generate a joint feature representation for fake news detection by integrating several feature extraction techniques commonly used in the literature. Experiments on fake news datasets show a significant improvement in classification performance compared to individual views (feature representations). We also observed that selecting a subset of the views instead of composing a latent space with all the views can be advantageous in terms of accuracy and computational effort. For further details, including source codes, figures, and datasets, please refer to the project's repository: https://github.com/ingrydpereira/multiview-fake-news.

Related papers

• VMID: A Multimodal Fusion LLM Framework for Detecting and Identifying Misinformation of Short Videos [14.551693267228345]
  This paper presents a novel fake news detection method based on multimodal information, designed to identify misinformation through a multi-level analysis of video content. The proposed framework successfully integrates multimodal features within videos, significantly enhancing the accuracy and reliability of fake news detection.
  arXiv  Detail & Related papers  (2024-11-15T08:20:26Z)
• FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detection [54.37159298632628]
  FineFake is a multi-domain knowledge-enhanced benchmark for fake news detection. FineFake encompasses 16,909 data samples spanning six semantic topics and eight platforms. The entire FineFake project is publicly accessible as an open-source repository.
  arXiv  Detail & Related papers  (2024-03-30T14:39:09Z)
• Adapting Fake News Detection to the Era of Large Language Models [48.5847914481222]
  We study the interplay between machine-(paraphrased) real news, machine-generated fake news, human-written fake news, and human-written real news. Our experiments reveal an interesting pattern that detectors trained exclusively on human-written articles can indeed perform well at detecting machine-generated fake news, but not vice versa.
  arXiv  Detail & Related papers  (2023-11-02T08:39:45Z)
• AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection [53.448283629898214]
  The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous work on detecting AI-generated fake videos only utilize visual modality or audio modality. We propose an Audio-Visual Transformer-based Ensemble Network (AVTENet) framework that considers both acoustic manipulation and visual manipulation.
  arXiv  Detail & Related papers  (2023-10-19T19:01:26Z)
• Semantic Prompt for Few-Shot Image Recognition [76.68959583129335]
  We propose a novel Semantic Prompt (SP) approach for few-shot learning. The proposed approach achieves promising results, improving the 1-shot learning accuracy by 3.67% on average.
  arXiv  Detail & Related papers  (2023-03-24T16:32:19Z)
• 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)
• TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents [51.744527199305445]
  This paper proposes a unified end-to-end information extraction framework from visually rich documents. Text reading and information extraction can reinforce each other via a well-designed multi-modal context block. The framework can be trained in an end-to-end trainable manner, achieving global optimization.
  arXiv  Detail & Related papers  (2022-07-14T08:52:07Z)
• 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)
• Feature Extraction of Text for Deep Learning Algorithms: Application on Fake News Detection [0.0]
  It will be shown that by using deep learning algorithms and alphabet frequencies of the original text of a news without any information about the sequence of the alphabet can actually be used to classify fake news and trustworthy ones in high accuracy. It seems that alphabet frequencies contains some useful features for understanding complex context or meaning of the original text.
  arXiv  Detail & Related papers  (2020-10-12T07:43:01Z)
• TRIE: End-to-End Text Reading and Information Extraction for Document Understanding [56.1416883796342]
  We propose a unified end-to-end text reading and information extraction network. multimodal visual and textual features of text reading are fused for information extraction. Our proposed method significantly outperforms the state-of-the-art methods in both efficiency and accuracy.
  arXiv  Detail & Related papers  (2020-05-27T01:47:26Z)
• SAFE: Similarity-Aware Multi-Modal Fake News Detection [8.572654816871873]
  We propose a new method to detect fake news based on its text, images, or their "mismatches" Such representations of news textual and visual information along with their relationship are jointly learned and used to predict fake news. We conduct extensive experiments on large-scale real-world data, which demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2020-02-19T02:51:04Z)

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.