计算机科学 ›› 2022, Vol. 49 ›› Issue (1): 101-107.doi: 10.11896/jsjkx.201200007
所属专题： 大数据&数据科学 虚拟专题
陈志毅, 隋杰
CHEN Zhi-yi, SUI Jie
摘要： 随着以微博为代表的社交媒体越来越流行,谣言信息借助社交媒体迅速传播,容易造成严重的后果,因此自动谣言检测问题受到了国内外学术界、产业界的广泛关注。目前,越来越多的用户使用图片来发布微博,而不仅仅是文本,微博通常由文本、图像和社会语境组成。因此,文中提出了一种基于深度神经网络,针对配文文本内容、图像以及用户属性信息的多模态网络谣言检测方法DCNN。该方法由多模态特征提取器和谣言检测器组成,多模态特征提取器分为3部分,即基于TextCNN的文本特征提取器、基于VGG-19的图片特征提取器和基于DeepFM算法的用户社会特征提取器,分别用于学习微博不同模态上的特征表示,以形成重新参数化的多模态特征,特征融合后将该融合后的多模态特征作为谣言检测器的输入进行分类检测。在微博数据集上对该算法进行了大量实验,实验结果表明DCNN算法将识别准确率从78.1%提高到了80.3%,验证了DCNN算法和其中对社会特征建立特征交互方法的可行性与有效性。
中图分类号:
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