A Big Data-empowered System for Real-time Detection of Regional Discriminatory Comments on Vietnamese Social Media

TL;DR

This paper presents a real-time system using machine learning and transfer learning to detect regional discriminatory comments on Vietnamese social media, supported by a new dataset and scalable streaming architecture.

Contribution

It introduces the ViRDC dataset and a scalable, real-time detection system built on Apache Spark for regional discrimination comments in Vietnam.

Findings

Developed the ViRDC dataset for regional discrimination in Vietnamese social media.

Built a real-time detection system with streaming capabilities.

Demonstrated system scalability and responsiveness in processing social media data.

Abstract

Regional discrimination is a persistent social issue in Vietnam. While existing research has explored hate speech in the Vietnamese language, the specific issue of regional discrimination remains under-addressed. Previous studies primarily focused on model development without considering practical system implementation. In this work, we propose a task called Detection of Regional Discriminatory Comments on Vietnamese Social Media, leveraging the power of machine learning and transfer learning models. We have built the ViRDC (Vietnamese Regional Discrimination Comments) dataset, which contains comments from social media platforms, providing a valuable resource for further research and development. Our approach integrates streaming capabilities to process real-time data from social media networks, ensuring the system's scalability and responsiveness. We developed the system on the Apache Spark framework to efficiently handle increasing data inputs during streaming. Our system offers a comprehensive solution for the real-time detection of regional discrimination in Vietnam.