Multimodal Bilinear Pooling Method for Fake News Detection

Abstract

Abstract: The rise of social media has promoted the development of the news industry and made the spread of fake news more convenient. However, diversified news expressions have brought many negative effects, such as news content exaggerating facts, malicious tampering of news text or image content, the construction of fake news facts arousing public opinion, which makes fake news detection a new challenge in the news field. In order to deal with the research of fake news detection work, the news text and image information are combined, the traditional feature fusion method is changed through the multimodal bilinear pooling method, and a fake news detection model based on the new feature fusion method is constructed. The standard data set in the detection field verifies the performance of the model. The experimental results show that the fusion feature of text and image is irreplaceable in the field of fake news detection, and the proposed method can effectively improve the performance of fake news detection.

Key words: fake news detection, social multimedia, multimodal feature fusion, bilinear pooling, deep learning

LI Guo-dong, PENG Dun-lu. Multimodal Bilinear Pooling Method for Fake News Detection[J]. Computer and Modernization, 2021, 0(11): 17-21.

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