Role of Artificial Intelligence in Detection of Hateful Speech for Hinglish Data on Social Media

TL;DR

This paper presents a novel AI-based approach for detecting hate speech in Hinglish social media data using contextual embeddings, significantly improving detection accuracy over existing methods.

Contribution

It introduces a fine-tuning methodology employing ELMo, FLAIR, and BERT for Hinglish hate speech detection, addressing the challenge of code-mixed language analysis.

Findings

Proposed model outperforms existing methods in accuracy

Utilizes contextual embeddings for better language understanding

Effective on multiple Hinglish datasets

Abstract

Social networking platforms provide a conduit to disseminate our ideas, views and thoughts and proliferate information. This has led to the amalgamation of English with natively spoken languages. Prevalence of Hindi-English code-mixed data (Hinglish) is on the rise with most of the urban population all over the world. Hate speech detection algorithms deployed by most social networking platforms are unable to filter out offensive and abusive content posted in these code-mixed languages. Thus, the worldwide hate speech detection rate of around 44% drops even more considering the content in Indian colloquial languages and slangs. In this paper, we propose a methodology for efficient detection of unstructured code-mix Hinglish language. Fine-tuning based approaches for Hindi-English code-mixed language are employed by utilizing contextual based embeddings such as ELMo (Embeddings for Language Models), FLAIR, and transformer-based BERT (Bidirectional Encoder Representations from Transformers). Our proposed approach is compared against the pre-existing methods and results are compared for various datasets. Our model outperforms the other methods and frameworks.