Automated Detection of Anti-National Textual Response to Terroristic Events on Online Media

The advent of internet has led to prodigious growth in popularity of social media platforms for people to communicate and opinionate on topics of their interests. And, terroristic events being a topic of national importance, receives enormous response from the citizens. Unfortunately, miscreants with anti-national agendas incite the large available audience on these platforms against the country by inducing anti-national content amid terrorist attacks. The social media platforms being of informal use are commonly observed to have users opinionating using multiple languages in same sentence called code-mixing. The over-arching goal of research done is to identify anti-national code-mix textual content on YouTube in the form of comments on terrorism-related videos. We collected YouTube comments on videos related to terroristic events in Kashmir region of India, which consisted of code-mix comments in Hindi (native language of India) and English languages. The paper presents a novel deep-learning-based transformer model HE-CM-BERT, i.e. Hindi-English code-mix BERT, where we extend the vocabulary of pre-trained multilingual BERT with code-mix vocabulary extracted from the collected data to automate the detection of anti-national code-mix text. The comparative analysis of the proposed model with the state-of-the-art machine learning and deep learning models depicts that it outperforms the existing ones.

Automated summary

The rise of the internet has led to the popularity of social media platforms where people can communicate and express their opinions on topics of interest. However, some individuals misuse these platforms to spread anti-national content, leading to hostility towards the country. This study aims to detect and monitor the presence of anti-national code-mixing text in comments on terrorism-related videos on YouTube using a novel deep-learning-based model.