Detecting Dravidian Offensive Posts in MIoT: A Hybrid Deep Learning Framework

Abstract

Hate speech and Offensive Posts (OP) detection on Smart Multimedia Internet of Things (MIoT) have been an active issue for researchers. MIoT media texts in non-native English-speaking countries are often code-mixed or script mixed/switched. This paper proposes an ensemble-based Deep Learning (DL) framework comprised of a Convolutional Neural Network (CNN) and a Dense Neural Network (DNN) for identifying hate and OP in Malayalam Code-Mixed (MCM), Tamil Code-Mixed (TCM), and Malayalam Script-Mixed (MSM) MIoT media postings. Word-level and character-level features are utilized in the convolutional neural network. In contrast, the dense neural network uses character-level Term Frequency-Inverse Document Frequency (TF-IDF) features. The inclusion of character-level features in the proposed ensemble framework resulted in state-of-the-art performance for TCM and MCM datasets, with weighted F 1 -score of 0.91 and 0.78, respectively, and comparable performance for MSM posts, with a weighted F 1 -score of 0.95.