Threatening Expression and Target Identification in Under-Resource Languages Using NLP Techniques

Lecture Notes in Computer Science. 2024. Vol. 14486. P. 3–17.

Malik M. S.

In recent decades, hate speech on social media platforms has been on
the rise. It is highly desired to control this kind of material because it initiates
unrest and harms to the society. Literature describes several forms of the hate
speech and it is quite challenging to differentiate between these forms and to
design an automated detection system, especially for under-resource languages.
In this study, we propose a robust framework for threatening expressions and

its target identification in Urdu (Nastaliq style) language. The proposed method-
ology presents each step in detail like data collection & annotation, cleaning &

pre-processing step, and fine-tuning of Robustly Optimized Bidirectional Encoder
Representations from Transformer (Urdu-RoBERTa) with grid search technique
for hyper-parameters optimization. The study exploits the strength of a pre-trained
Urdu-RoBERTa as a transfer learning technique with grid search fine-tuning. The

proposed framework is compared with state-of-the art baseline and ten compa-
rable models and it outperformed all for both tasks (threatening expression and

target identification). Furthermore, the proposed framework obtained benchmark
performance and improved the f1-score with substantial margin.

Keywords: Target identification, low-resource, hyper-parameters, RoBERTa, threatening expression, Natural Language Processing

Publication based on the results of: Models and methods for natural language processing, recommendation systems and data mining (2024)