基于多模双线性池化方法的虚假新闻检测模型

社交媒体的兴起促进了新闻行业的发展，使虚假新闻的传播也变得更为便利，然而多样化的新闻表现形式带来了很多负面影响，比如新闻内容夸大事实、恶意篡改新闻文本或图像内容、构造虚假新闻事实引起社会舆论，这促使了虚假新闻检测工作成为新闻领域新的挑战。为了应对虚假新闻检测工作的研究，将新闻文本与图像信息结合起来，通过多模双线性池化方法，改变传统特征融合方法，构建出基于新特征融合方法的虚假新闻检测模型，并且采用虚假新闻检测领域标准数据集验证模型的性能，实验结果表明，文本与图像的融合特征表现在虚假新闻检测领域不可替代，且所提方法能够有效提升虚假新闻检测性能。

Multimodal Bilinear Pooling Method for Fake News Detection

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.