Detection Of Extremist Using A Deep Learning-Based Sentiment Analysis: A Case Study On Afan Oromo Social Media Posts

Abstract

Social media is a system for electronic communications that allows users to establish online communication platforms. Most popular social media platforms include: Facebook, Twitter, YouTube and other commonly used social blogging platforms. The number of people using these social media is increasing rapidly and it's important to be aware of what comments and posts are being shared. As users share their ideas without control on these sites, the spread of extremist ideas and offensive language becomes a great challenge. In spite of being the source of inciting and inflaming content, extremists can go undetected for a long period of time due to the huge amount of online data and the inefficiency of manual detection strategies that have been practiced in many developing countries like Ethiopia. For resource-rich western languages like English, a number of studies have been conducted on social media analytics and social network analysis in relation to detecting extremists using various machine learning and deep learning techniques. However, to the best of our knowledge, no formal research has been conducted on Afan Oromo social media comments and posts to automatically detect extremist and conflict-inciting social media platforms such as Facebook and Twitter. We have proposed a deep learning-based sentiment analysis solution that detects extremist texts on social media based on users' comments and posts in Afan Oromo. The main purpose of this research work is to design and develop a model that automatically detects extremist content that is commented and posted in Afan Oromo text on social media such as Facebook. In the first step, we collected comments and posts from the public Facebook pages of BBC, OBN, FBC, OLF, KFO, and politically influential people using the Facepager tool. In the second step, text preprocessing tasks are applied and data annotation tasks are accomplished in consultation with domain experts and Afan Oromo experts. In the third step, fast-text word embedding was applied for features representations. In the fourth step, we loaded 80% of the dataset for training deep learning models LSTM, CNN, LSTM+CNN, and CNN+LSTM to build an extremist detection model, and we compared their performances in our experiment. Finally, the proposed CNN+LSTM model scored an 91% accuracy with fast-text word embedding, which is a promising performance for detections of extremist model on social media in Afan Oromo comments and posts.