Rumor detection model based on multimodal machine learning

Lulu Gao; Yali Gao; Jie Yuan; Xiaoyong Li

doi:10.1117/12.2678984

8 May 2023 Rumor detection model based on multimodal machine learning

Lulu Gao, Yali Gao, Jie Yuan, Xiaoyong Li

Proceedings Volume 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023); 126351H (2023) https://doi.org/10.1117/12.2678984
Event: International Conference on Algorithms, Microchips, and Network Applications 2023, 2023, Zhengzhou, China

Abstract

Traditional rumor detection methods that only focus on text content have achieved certain results. However, with the rapid development of social platforms, graphic information has occupied a large proportion. In this scenario, traditional detection methods cannot make full use of picture information for rumor detection. Aiming at the above scenarios, a rumor detection model integrating multi-modal features is proposed. Firstly, text features and visual features as well as their hidden states are extracted by using the pre-trained deep learning model, and then the preliminary fusion features are obtained by integrating the hidden states of text and image through the attention mechanism. Then, the text features, preliminary fusion features and social features are spliced, and the image features, preliminary fusion features and social features are spliced to obtain two final fusion features. Then the two features are input into different full connection layers to get their respective prediction results. Finally, the two prediction results are integrated to obtain the final detection results. Experimental results show that the proposed model is effective in detecting multimodal rumor data.

Citation Download Citation

Lulu Gao, Yali Gao, Jie Yuan, and Xiaoyong Li "Rumor detection model based on multimodal machine learning", Proc. SPIE 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023), 126351H (8 May 2023); https://doi.org/10.1117/12.2678984

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KEYWORDS

Feature extraction

Visualization

Semantics

Data modeling

Feature fusion

Machine learning

Classification systems

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