GraMuFeN: graph-based multi-modal fake news detection in social media

Kananian, Makan; Badiei, Fatemeh; Gh. Ghahramani, S. AmirAli

doi:10.1007/s13278-024-01267-0

Soc. Netw. Anal. Min.

2024

DOI: 10.1007/s13278-024-01267-0

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GraMuFeN: graph-based multi-modal fake news detection in social media

Makan Kananian,

Fatemeh Badiei,

S. AmirAli Gh. Ghahramani

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Cited by 3 publications

References 28 publications

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DPSG: Dynamic Propagation Social Graphs for multi-modal fake news detection

Jing,

Gao,

Zhang

et al. 2025

Information Fusion

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DPSG: Dynamic Propagation Social Graphs for multi-modal fake news detection

Jing,

Gao,

Zhang

et al. 2025

Information Fusion

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Multimodal Social Media Fake News Detection Based on 1D-CCNet Attention Mechanism

Yan,

Fu,

2024

Electronics

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Due to the explosive rise of multimodal content in online social communities, cross-modal learning is crucial for accurate fake news detection. However, current multimodal fake news detection techniques face challenges in extracting features from multiple modalities and fusing cross-modal information, failing to fully exploit the correlations and complementarities between different modalities. To address these issues, this paper proposes a fake news detection model based on a one-dimensional CCNet (1D-CCNet) attention mechanism, named BTCM. This method first utilizes BERT and BLIP-2 encoders to extract text and image features. Then, it employs the proposed 1D-CCNet attention mechanism module to process the input text and image sequences, enhancing the important aspects of the bimodal features. Meanwhile, this paper uses the pre-trained BLIP-2 model for object detection in images, generating image descriptions and augmenting text data to enhance the dataset. This operation aims to further strengthen the correlations between different modalities. Finally, this paper proposes a heterogeneous cross-feature fusion method (HCFFM) to integrate image and text features. Comparative experiments were conducted on three public datasets: Twitter, Weibo, and Gossipcop. The results show that the proposed model achieved excellent performance.

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Cross-Task Rumor Detection: Model Optimization Based on Model Transfer Learning and Graph Convolutional Neural Networks (GCNs)

Jiang,

Yan,

Ren

et al. 2024

Electronics

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With the widespread adoption of social media, the rapid dissemination of rumors poses a severe threat to public perception and social stability, emerging as a major challenge confronting society. Hence, the development of efficient and accurate rumor detection models has become an urgent need. Given the challenges of rumor detection tasks, including data scarcity, feature complexity, and difficulties in cross-task knowledge transfer, this paper proposes a BERT–GCN–Transfer Learning model, an integrated rumor detection model that combines BERT (Bidirectional Encoder Representations from Transformers), Graph Convolutional Networks (GCNs), and transfer learning techniques. By harnessing BERT’s robust text representation capabilities, the GCN’s feature extraction prowess on graph-structured data, and the advantage of transfer learning in cross-task knowledge sharing, the model achieves effective rumor detection on social media platforms. Experimental results indicate that this model achieves accuracies of 0.878 and 0.892 on the Twitter15 and Twitter16 datasets, respectively, significantly enhancing the accuracy of rumor detection compared to baseline models. Moreover, it greatly improves the efficiency of model training.

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GraMuFeN: graph-based multi-modal fake news detection in social media

Cited by 3 publications

References 28 publications

DPSG: Dynamic Propagation Social Graphs for multi-modal fake news detection

DPSG: Dynamic Propagation Social Graphs for multi-modal fake news detection

Multimodal Social Media Fake News Detection Based on 1D-CCNet Attention Mechanism

Cross-Task Rumor Detection: Model Optimization Based on Model Transfer Learning and Graph Convolutional Neural Networks (GCNs)

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