Citation

Kumari K, Singh JP. Multimed. Syst. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, Association for Computing Machinery, Publisher Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s00530-021-00785-7

PMID

unavailable

Abstract

Aggressive comments containing offensive images and inappropriate gesture signs together with textual comments have grown exponentially in the recent past on social media. These aggressive contents on social media are affecting the victims negatively causing fear, stress, sleeping problems and even suicide in some cases. Since social media contents are unmoderated, a technical solution with the characteristic of having automatic flagging of these contents considering the text and images together is highly needed. This article presents a deep learning and binary firefly-based optimization-based model to classify the social media posts into high-aggressive, medium-aggressive, and non-aggressive classes. The proposed model considers both text and images together to evaluate the aggression level of a post. In this model, the image features of the posts are extracted using pre-trained VGG-16 model, whereas the textual features are extracted using a three-layered convolutional neural network in parallel. The image and text features are then combined to get a hybrid feature set which is further optimized using a binary firefly optimization algorithm. Our proposed model improves the results by 11% in terms of the weighted F1-score with optimized features by binary firefly algorithm over non-optimized features.

Language: en

Keywords

Binary firefly optimization; Binary particle optimization; Convolution neural network; Cyber-aggression; Cyberbullying; Feature optimization