Geo-Spatial Mapping of Hate Speech Prediction in Roman Urdu

Samia Aziz, Muhammad Shahzad Sarfraz (), Muhammad Usman, Muhammad Umar Aftab and Hafiz Tayyab Rauf ()
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Samia Aziz: Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan
Muhammad Shahzad Sarfraz: Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan
Muhammad Usman: Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan
Muhammad Umar Aftab: Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan
Hafiz Tayyab Rauf: Independent Researcher, Bradford BD8 0HS, UK

Mathematics, 2023, vol. 11, issue 4, 1-26

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.

Keywords: natural language processing; machine learning; deep learning; spatial analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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