Article: Social network news dissemination and detection model based on multi-scale information Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.6 pp.1 - 26 Abstract: At a critical stage of social network information evolution, the rapid spread of false news, emotional content, and rumours poses significant challenges to information governance. To address this issue, a multi-scale social network news dissemination and detection model is proposed. The model integrates multi-scale feature extraction, cascaded convolutional networks, and cross-modal information modelling to enhance feature representation and propagation pattern capture. Experimental results show that introducing macro-micro dual-scale modelling and gated fusion improves the F1 score to 0.895 and reduces the mean absolute percentage error to 8.9%, representing gains of 7.8 and 3.4 percentage points over single-scale baselines (p < 0.01). Across diverse communication scenarios, the model consistently outperforms comparison methods, achieving macro-F1 scores of 86%-91% and micro-F1 scores of 88%-92%. With an average detection delay of approximately 12 ms, the model balances real-time performance and robustness, demonstrating effectiveness and stability for multi-scenario news detection. Inderscience Publishers - linking academia, business and industry through research

Title: Social network news dissemination and detection model based on multi-scale information

Authors: Yanli Zhang

Addresses: Publicity Department, Henan Open University, Zhengzhou, 450046, China

Abstract: At a critical stage of social network information evolution, the rapid spread of false news, emotional content, and rumours poses significant challenges to information governance. To address this issue, a multi-scale social network news dissemination and detection model is proposed. The model integrates multi-scale feature extraction, cascaded convolutional networks, and cross-modal information modelling to enhance feature representation and propagation pattern capture. Experimental results show that introducing macro-micro dual-scale modelling and gated fusion improves the F1 score to 0.895 and reduces the mean absolute percentage error to 8.9%, representing gains of 7.8 and 3.4 percentage points over single-scale baselines (p < 0.01). Across diverse communication scenarios, the model consistently outperforms comparison methods, achieving macro-F1 scores of 86%-91% and micro-F1 scores of 88%-92%. With an average detection delay of approximately 12 ms, the model balances real-time performance and robustness, demonstrating effectiveness and stability for multi-scenario news detection.

Keywords: news detection; multi-scale feature extraction; cascaded convolutional network; cross-modal information; gating mechanism.

DOI: 10.1504/IJICT.2026.151560

International Journal of Information and Communication Technology, 2026 Vol.27 No.6, pp.1 - 26

Received: 10 Oct 2025
Accepted: 01 Dec 2025
Published online: 06 Feb 2026 *

Title: Social network news dissemination and detection model based on multi-scale information

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