BREE-HD: A Transformer-Based Model to Identify Threats on Twitter

Authors

Sinchana Kumbale, Manipal Institute of Technology
Smriti Singh, The University of Texas at Austin
G. Poornalatha, Manipal Institute of Technology
Sanjay Singh, Manipal Institute of Technology

Document Type

Article

Publication Title

IEEE Access

Abstract

With the world transitioning to an online reality and a surge in social media users, detecting online harassment and threats has become more pressing than ever. Gendered cyber-hate causes women significant social, psychological, reputational, economic, and political harm. To tackle this problem, we develop a dataset and propose a transformer-based model to classify tweets into threats or non-threats that are either sexist or non-sexist. We have developed a model to identify sexist and non-sexist threats from a collection of sexist, non-sexist tweets. BREE-HD performs extraordinarily well with an accuracy of 97% when trained on the dataset we developed to detect threats from a collection of derogatory tweets. To provide insight into how BREE-HD makes classifications, we apply explainable A.I. (XAI) concepts to provide a detailed qualitative analysis of our proposed methodology. As an extension of our work, BREE-HD could be used as a part of a system that could detect threats targeting people specifically tailored to classify them in real-time adequately.

First Page

67180

Last Page

67190

DOI

10.1109/ACCESS.2023.3291072

Publication Date

1-1-2023

Recommended Citation

Kumbale, Sinchana; Singh, Smriti; Poornalatha, G.; and Singh, Sanjay, "BREE-HD: A Transformer-Based Model to Identify Threats on Twitter" (2023). Open Access archive. 6112.
https://impressions.manipal.edu/open-access-archive/6112