@inproceedings{208f3a291f5648f09ddbfaa74aaf62f7,
title = "A Text-mining approach for crime tweets in Saudi Arabia: From analysis to prediction",
abstract = "Social networks have proven to be a massive hub for investigating contextual and individual behavior of people. Most recently micro-blogging sites like Twitter are indicating to researchers that their content can be aggregated and used to effectively predict forecast, and infer outcomes of real-world events. The crime-related tweets analysis research in Saudi Arabia set off with an ultimate goal of gathering a deeper understanding of what kinds of criminal weapons are people frequently talking about. In this paper, we aim at dealing with tweets mentioning different weapons, analyzing them to gather facts such as annual variation of percentage tweets mentioning different weapons, recognizing the impact of events such as the Covid-19 pandemic on crime social discussions. In the following step, we develop a number of classifiers to predict which weapon is mentioned in a tweet. In order to perform our tasks, the Python programming language is used in the majority of the cases.",
keywords = "analysis, crime, Deep Learning, Machine Learning, prediction, Saudi Arabia, text mining, Twit-ter",
author = "Amal Algefes and Nouf Aldossari and Fatma Masmoudi and Elham Kariri",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 7th International Conference on Data Science and Machine Learning Applications, CDMA 2022 ; Conference date: 01-03-2022 Through 03-03-2022",
year = "2022",
doi = "10.1109/CDMA54072.2022.00023",
language = "English",
series = "Proceedings - 2022 7th International Conference on Data Science and Machine Learning Applications, CDMA 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "109--114",
booktitle = "Proceedings - 2022 7th International Conference on Data Science and Machine Learning Applications, CDMA 2022",
address = "United States",
}