Geo-Spatial Mapping of Hate Speech Prediction in Roman Urdu

Abstract

Social media has transformed into a crucial channel for political expression. Twitter, especially, is a vital platform used to exchange political hate in Pakistan. Political hate speech affects the public image of politicians, targets their supporters, and hurts public sentiments. Hate speech is a controversial public speech that promotes violence toward a person or group based on specific characteristics. Although studies have been conducted to identify hate speech in European languages, Roman languages have yet to receive much attention. In this research work, we present the automatic detection of political hate speech in Roman Urdu. An exclusive political hate speech labeled dataset (RU-PHS) containing 5002 instances and city-level information has been developed. To overcome the vast lexical structure of Roman Urdu, we propose an algorithm for the lexical unification of Roman Urdu. Three vectorization techniques are developed: TF-IDF, word2vec, and fastText. A comparative analysis of the accuracy and time complexity of conventional machine learning models and fine-tuned neural networks using dense word representations is presented for classifying and predicting political hate speech. The results show that a random forest and the proposed feed-forward neural network achieve an accuracy of 93% using fastText word embedding to distinguish between neutral and politically offensive speech. The statistical information helps identify trends and patterns, and the hotspot and cluster analysis assist in pinpointing Punjab as a highly susceptible area in Pakistan in terms of political hate tweet generation.

Author Profiling from Short Romanized Urdu Messages: A Preliminary Investigation using Transfer Learning Models

“…Each chosen model is trained to classify gender and age groups based on the embedded features derived from the previous step. To enhance the training process, we employ suitable training techniques, define appropriate loss functions to measure model performance, and utilize optimization algorithms to fine-tune the models [23]. In order to achieve optimal performance and make sure that the models can accurately categorize gender and age categories in the dataset, a rigorous training phase is essential.…”