A Fake News Detection Approach Enhanced by Multi-Source Feature Fusion

HU Ze; CHEN Zhinan; YANG Hongyu

doi:10.11999/JEIT250041

Volume 47 Issue 8

Aug. 2025

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HU Ze, CHEN Zhinan, YANG Hongyu. A Fake News Detection Approach Enhanced by Multi-Source Feature Fusion[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2919-2934. doi: 10.11999/JEIT250041

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HU Ze, CHEN Zhinan, YANG Hongyu. A Fake News Detection Approach Enhanced by Multi-Source Feature Fusion[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2919-2934. doi: 10.11999/JEIT250041

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A Fake News Detection Approach Enhanced by Multi-Source Feature Fusion

doi: 10.11999/JEIT250041 cstr: 32379.14.JEIT250041

HU Ze¹,
CHEN Zhinan¹,
YANG Hongyu^{1, 2
,
,}

1.
School of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China
2.
School of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China

Funds: The National Natural Science Foundation of China (62201576, U2433205), The Supporting Fund of the National Natural Science Foundation of China (3122023PT10)

Received Date: 2025-01-16
Rev Recd Date: 2025-05-21

Available Online: 2025-06-14

Publish Date: 2025-08-27

Abstract

Abstract

Objective News exhibits multidimensional complexity, comprising structural, temporal, and content features. Structural features are reflected in the propagation path, depth, and breadth. Fake news often exhibits distinctive structural patterns, such as rapid diffusion through a limited number of “opinion leader” nodes or the formation of densely connected propagation clusters. Temporally, fake news tends to spread quickly within short timeframes, characterized by unusually high dissemination speeds and elevated interaction rates. Content features include the information conveyed in headlines and body text; fake news often contains sensationalized headlines, emotive language, inaccurate data, or fabricated claims. Detection models that rely solely on a single feature type often demonstrate limited discriminative performance. Therefore, capturing the hierarchical and heterogeneous attributes of news is critical to improving detection accuracy and remains a major focus of ongoing research. Current approaches predominantly emphasize content features, with limited incorporation of structural characteristics. Although Graph Neural Networks (GNN) have been employed to model propagation structures, their integration of content and temporal information remains inadequate. To address these limitations, this study proposes a fake news detection approach based on multi-source feature fusion, which enables more comprehensive feature representation and substantially enhances detection performance. Methods To enhance fake news detection performance, this study proposes a Multi-Source Feature Fusion Enhancement (MSFFE) approach that extracts features from three sources: structural, temporal, and content, and integrates them using an adaptive fusion mechanism. This mechanism dynamically adjusts the weight of each feature type to generate a unified representation with comprehensive expressiveness. The model comprises three core components: propagation tree encoding, multi-source feature extraction, and news classification (Fig. 2). In the propagation tree encoding component, a GNN is employed to represent the news propagation structure. Specifically, the GraphSAGE (Graph SAmple and aggreGatE) algorithm is used to aggregate node information from the propagation tree to the root node, enabling efficient capture of local structural patterns and temporal dynamics. Compared with conventional GNN methods, GraphSAGE improves scalability for large-scale graphs and reduces computational complexity by avoiding full-graph updates. In the multi-source feature extraction component, the model extracts structural, temporal, and content features. For structural features, the encoded propagation tree nodes are organized into a hypergraph. A hypergraph attention mechanism is then applied: first, hyperedge representations are updated via node-level attention; next, node representations are updated via hyperedge-level attention; and finally, structural-level features are obtained. For temporal features, node activity across multiple time windows is modeled using time-scale expansion and compression. A time decay attention mechanism is introduced to extract multi-scale temporal features, which are then fused into a unified temporal representation. For content features, the root node’s associated text is processed using a multi-head self-attention mechanism to capture semantic information, yielding content-level features. After extracting the three feature types, an adaptive multi-source fusion mechanism integrates them into a final news representation. This representation is passed through a fully connected layer and activation function for classification. The fully connected layer applies a linear transformation using a learnable weight matrix and bias term to produce predicted scores for each news instance. During training, model parameters are optimized to maximize classification accuracy. The final output is mapped to a probability in [0,1] using a Sigmoid activation function, indicating the likelihood of the news being classified as “true” or “fake.” A threshold of 0.5 is used for binary classification: probabilities above 0.5 are labeled “fake,” and those below are labeled “true.” Results and Discussions As shown in Table 3, the ablation experiments demonstrate that incorporating features from different sources into the base model significantly improves fake news detection accuracy. This finding confirms the effectiveness of the core components in the proposed approach. The integration of multi-source features enhances the overall detection performance, highlighting the advantage of the fusion mechanism in identifying fake news. Comparative experiments further support these results. As shown in (Table 2), the proposed approach outperforms existing approaches on both the Politifact and Gossipcop datasets. On the Politifact dataset, it improves accuracy by 3.64% and the F1 score by 3.41% compared with the State-Of-The-Art (SOTA) method Robust Trust Evaluation Architecture (RTRUST). On the Gossipcop dataset, the accuracy and F1 score increase by 0.55% and 0.56%, respectively. These improvements are attributed to the approach’s ability to effectively model high-order structural features and integrate temporal and content features, resulting in more comprehensive and discriminative feature representations. Conclusions Experimental results demonstrate that the proposed approach effectively extracts and fuses multi-source features, substantially improving the performance of fake news detection. By enhancing the model’s ability to represent structural, temporal, and content characteristics, the approach contributes to more accurate classification. This has the potential to mitigate the societal consequences of fake news, including public misinformation, reputational damage to organizations, and policy misjudgments.
- Fake news detection,
- Hypergraph,
- Graph Neural Networks (GNNs),
- Attention mechanism,
- Multi-feature fusion

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