Fine-Grained Arabic Post (Tweet) Geolocation Prediction Using Deep Learning Techniques - Marwa K. Elteir (Information)
Leveraging Twitter data for crisis management necessitates the accurate, fine-grained geolocation of tweets, which unfortunately is often lacking, with only 1–3% of tweets being geolocated. This work addresses the understudied problem of fine-grained geolocation prediction for Arabic tweets, focusing on the Kingdom of Saudi Arabia. The goal is to accurately assign tweets to one of thirteen provinces. Existing approaches for Arabic geolocation are limited in accuracy and often rely on basic machine learning techniques. Additionally, advancements in tweet geolocation for other languages often rely on distinct datasets, hindering direct comparisons and assessments of their relative performance on Arabic datasets. To bridge this gap, we investigate eight advanced deep learning techniques, including two Arabic pretrained language models (PLMs) on one constructed dataset. Through a comprehensive analysis, we assess the strengths and weaknesses of each technique for fine-grained Arabic tweet geolocation. Despite the success of PLMs in various tasks, our results demonstrate that a combination of Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) layers yields the best performance, achieving a test accuracy of 93.85%.