Abstract
Twitter, one of the most popular microblogging platforms, has tens of millions of active users worldwide, generating hundreds of millions of posts every day. Twitter posts, referred to as "tweets", the short and the noisy text, bring many challenges with them, such as in the case of some emergency or disaster. Predicting the location of these tweets is important for social, security, human rights, and business reasons and has raised noteworthy consideration lately. However, most Twitter users disable the geo-tagging feature, and their home locations are neither standardized nor accurate. In this study, we applied four machine learning techniques named Logistic Regression, Random Forest, Multinomial Naive Bayes, and Support Vector Machine with and without the utilization of the geo-distance matrix for location prediction of a tweet using its textual content. Our extensive experiments on our vast collection of Arabic tweets From Saudi Arabia with different feature sets yielded promising results with 67% accuracy.