Improving Rumor Detection by Image Captioning and Multi-Cell Bi-RNN With Self-Attention in Social Networks

User-generated contents in social media are not verified before being posted. They could bring many problems if they were misused. Among various types of rumors, the authors focus on the type in which there's mismatch between images and their surrounding texts. They can be detected by multimodal feature fusion in RNNs with attention mechanism, but the relations between images and texts are not well-addressed. In this paper, the authors propose to improve rumor detection by image captioning and RNNs with self-attention. Firstly, they utilize the idea of image captioning to translate images into the corresponding text descriptions. Secondly, these caption words are represented by word embedding models and aggregated with surrounding texts using early fusion. Finally, multi-cell bi-directional RNNs with self-attention are used to learn important features to identify rumors. From the experimental results, the best F-measure of 0.882 can be obtained, which shows the potential of our proposed approach to rumor detection. Further investigation is needed for data in larger scale.