GRaMuFeN: Graph-based Multi-modal Fake News Detection in Social Media

• URL: http://arxiv.org/abs/2310.07668v1
• Date: Wed, 11 Oct 2023 17:17:40 GMT
• Title: GRaMuFeN: Graph-based Multi-modal Fake News Detection in Social Media
• Authors: Makan Kananian, Fatima Badiei, S. AmirAli Gh. Ghahramani
• Abstract summary: We propose GraMuFeN, a model designed to detect fake content by analyzing both the textual and image content of news. GraMuFeN comprises two primary components: a text encoder and an image encoder. For textual analysis, GraMuFeN treats each text as a graph and employs a Graph Convolutional Neural Network (GCN) as the text encoder.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of social media platforms such as Twitter, Instagram, and Weibo has significantly enhanced the dissemination of false information. This phenomenon grants both individuals and governmental entities the ability to shape public opinions, highlighting the need for deploying effective detection methods. In this paper, we propose GraMuFeN, a model designed to detect fake content by analyzing both the textual and image content of news. GraMuFeN comprises two primary components: a text encoder and an image encoder. For textual analysis, GraMuFeN treats each text as a graph and employs a Graph Convolutional Neural Network (GCN) as the text encoder. Additionally, the pre-trained ResNet-152, as a Convolutional Neural Network (CNN), has been utilized as the image encoder. By integrating the outputs from these two encoders and implementing a contrastive similarity loss function, GraMuFeN achieves remarkable results. Extensive evaluations conducted on two publicly available benchmark datasets for social media news indicate a 10 % increase in micro F1-Score, signifying improvement over existing state-of-the-art models. These findings underscore the effectiveness of combining GCN and CNN models for detecting fake news in multi-modal data, all while minimizing the additional computational burden imposed by model parameters.

Related papers

• Enhancing Fake News Detection in Social Media via Label Propagation on Cross-modal Tweet Graph [19.409935976725446]
  We present a novel method for detecting fake news in social media. Our method densifies the graph's connectivity to capture denser interaction better. We use three publicly available fake news datasets, Twitter, PHEME, and Weibo, for evaluation.
  arXiv  Detail & Related papers  (2024-06-14T09:55:54Z)
• CNN2GNN: How to Bridge CNN with GNN [59.42117676779735]
  We propose a novel CNN2GNN framework to unify CNN and GNN together via distillation. The performance of distilled boosted'' two-layer GNN on Mini-ImageNet is much higher than CNN containing dozens of layers such as ResNet152.
  arXiv  Detail & Related papers  (2024-04-23T08:19:08Z)
• GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking [18.783663535152158]
  Graph-based techniques have incorporated this social context but are limited by the need for large labeled datasets. This paper introduces GAMC, an unsupervised fake news detection technique using the Graph Autoencoder with Masking and Contrastive learning. By leveraging both the context and content of news propagation as self-supervised signals, our method negates the requirement for labeled datasets.
  arXiv  Detail & Related papers  (2023-12-10T03:34:29Z)
• Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering [79.44443231700201]
  Multi-modal keyphrase generation aims to produce a set of keyphrases that represent the core points of the input text-image pair. The input text and image are often not perfectly matched, and thus the image may introduce noise into the model. We propose a novel multi-modal keyphrase generation model, which not only enriches the model input with external knowledge, but also effectively filters image noise.
  arXiv  Detail & Related papers  (2023-09-09T09:41:36Z)
• Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory [76.4580340399321]
  We propose ETGNN, a novel Evidential Temporal-aware Graph Neural Network. We construct view-specific graphs whose nodes are the texts and edges are determined by several types of shared elements respectively. Considering the view-specific uncertainty, the representations of all views are converted into mass functions through evidential deep learning (EDL) neural networks.
  arXiv  Detail & Related papers  (2022-05-24T16:22:40Z)
• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding [53.170767750244366]
  Imagen is a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. To assess text-to-image models in greater depth, we introduce DrawBench, a comprehensive and challenging benchmark for text-to-image models.
  arXiv  Detail & Related papers  (2022-05-23T17:42:53Z)
• GAME-ON: Graph Attention Network based Multimodal Fusion for Fake News Detection [6.037721620350107]
  We propose GAME-ON, a Graph Neural Network based end-to-end trainable framework to learn more robust data representations for multimodal fake news detection. Our model outperforms on Twitter by an average of 11% and keeps competitive performance on Weibo, within a 2.6% margin, while using 65% fewer parameters than the best comparable state-of-the-art baseline.
  arXiv  Detail & Related papers  (2022-02-25T03:27:37Z)
• FiLMing Multimodal Sarcasm Detection with Attention [0.7340017786387767]
  Sarcasm detection identifies natural language expressions whose intended meaning is different from what is implied by its surface meaning. We propose a novel architecture that uses the RoBERTa model with a co-attention layer on top to incorporate context incongruity between input text and image attributes. Our results demonstrate that our proposed model outperforms the existing state-of-the-art method by 6.14% F1 score on the public Twitter multimodal detection dataset.
  arXiv  Detail & Related papers  (2021-08-09T06:33:29Z)
• DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection [0.04834203844100679]
  We propose a knowleDgE grAPh FAKe nEws Detection framework for identifying Fake News. Our approach is a combination of the NLP -- where we encode the news content, and the GNN technique -- where we encode the Knowledge Graph. We evaluate our framework using two publicly available datasets containing articles from domains such as politics, business, technology, and healthcare.
  arXiv  Detail & Related papers  (2021-07-04T07:09:59Z)
• Very Deep Graph Neural Networks Via Noise Regularisation [57.450532911995516]
  Graph Neural Networks (GNNs) perform learned message passing over an input graph. We train a deep GNN with up to 100 message passing steps and achieve several state-of-the-art results.
  arXiv  Detail & Related papers  (2021-06-15T08:50:10Z)
• Neural Human Video Rendering by Learning Dynamic Textures and Rendering-to-Video Translation [99.64565200170897]
  We propose a novel human video synthesis method by explicitly disentangling the learning of time-coherent fine-scale details from the embedding of the human in 2D screen space. We show several applications of our approach, such as human reenactment and novel view synthesis from monocular video, where we show significant improvement over the state of the art both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2020-01-14T18:06:27Z)

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.