Emotion-Enhanced Multi-Modal Persuasive Techniques Detection Using Split Features

Abstract

The persuasive techniques in propaganda campaigns impact the Internet environment and our society. Detecting persuasive techniques has aroused broad attention in natural language processing field. In this paper, we propose a novel emotion-enhanced and multi-level representation learning approach for multi-modal persuasive techniques detection. To consider the emotional factors used in persuasive techniques, we embed the text and images using different networks, and use a fully connected emotion enhanced layer to fuse multi-modal embedding, where the type and strength of emotions are incorporated in the text embedding. To better model the multi-modal features in persuasive techniques, the fused features are inputted to a split-and-share module where multi-level representations are employed to obtain better detection performance. Furthermore, we integrate the focal loss to alleviate the problem of data imbalance for persuasive techniques detection. Experimental results on publicly used dataset show that the proposed model is effective for multi-modal persuasive techniques detection. Remarkable experimental results indicate the capability of our MPDES in extracting the deeper information contained in dual modalities.

Graph-based multi-information integration network with external news environment perception for Propaganda detection

“…However, they fail to model the different patterns of sentence interactions between trusted news and propaganda news (Vaibhav et al, 2019). Subsequently, multimodal learning methods were proposed to detect propaganda (Vijayaraghavan and Vosoughi, 2022;Tall'on-Ballesteros, 2022). They integrate the features from both the image and the text.…”