Improving Multimodal Data Labeling with Deep Active Learning for Post Classification in Social Networks

Abstract

Automatic user post classification is an important task in the field of social network analysis. Being effectively solved, post classification could be used for thematic user feed composition or inappropriate content identification. Commonly addressed by applying various Machine Learning approaches, the task often involves manual processes related to ground truth sourcing, which is known to be a hardly-scalable and increasingly expensive procedure. At the same time, Active Learning for automatic user post classification is a promising way to bridge such a gap, as it does not require massive ground truth availability aligning our research with the real world settings. In this work, we put our focus on leveraging textual and visual data modalities for the application of user post classification and investigate how batch size and batch normalization disabling techniques could affect active deep neural network learning process. We solve the problem of automatic user post classification by employing our novel multimodal neural network architecture with multi-head tunable loss function components. We show that the proposed approach, coupled with Active Learning, allows for the achievement of a significant classification performance boost in terms of crowd assessing resources as compared to the passive learning approaches.