Detecting Radical Text over Online Media using Deep Learning

TL;DR

This paper presents a deep learning approach using LSTM networks to detect radical content online, achieving high precision and aiding security agencies in moderating extremist material.

Contribution

It introduces an LSTM-based neural network model trained on a large, annotated dataset for more effective radical content detection compared to traditional machine learning methods.

Findings

Achieved 85.9% precision in radical content detection

Utilized a dataset of 61,601 annotated online records

Demonstrated effectiveness of deep learning over existing approaches

Abstract

Social Media has influenced the way people socially connect, interact and opinionize. The growth in technology has enhanced communication and dissemination of information. Unfortunately,many terror groups like jihadist communities have started consolidating a virtual community online for various purposes such as recruitment, online donations, targeting youth online and spread of extremist ideologies. Everyday a large number of articles, tweets, posts, posters, blogs, comments, views and news are posted online without a check which in turn imposes a threat to the security of any nation. However, different agencies are working on getting down this radical content from various online social media platforms. The aim of our paper is to utilise deep learning algorithm in detection of radicalization contrary to the existing works based on machine learning algorithms. An LSTM based feed forward neural network is employed to detect radical content. We collected total 61601 records from various online sources constituting news, articles and blogs. These records are annotated by domain experts into three categories: Radical(R), Non-Radical (NR) and Irrelevant(I) which are further applied to LSTM based network to classify radical content. A precision of 85.9% has been achieved with the proposed approach