DEEP LEARNING METHOD FOR IDENTIFYING PROPAGANDA IN TEXTUAL DATA

Abstract

The paper examines the application of deep learning techniques for the detection of propaganda in textual data. The effectiveness and limitations of various neural network architectures, such as Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and Transformers, are analyzed. Particular emphasis is placed on the incorporation of hierarchical attention mechanisms to enable a more comprehensive understanding of propaganda techniques across different levels of text, including word, sentence, and document levels.