Abstract: Nowadays, the huge usage of internet leads to tremendous information growth as a result of our daily activities that deal with different sources such as news articles, forums, websites, emails and social media. Social media is a rich source of information that deeply affect users by its useful content. However, there are a lot of rumors in these social media platforms which can cause critical consequences to the people’s lives, especially if it is related to the health-related information. Several studies focused on automatically detecting rumors from social media by applying machine learning and intelligent methods. However, few studies concerned about health-related rumors in Arabic language. Therefore, this paper is dealing with detecting health-related rumors focusing on cancer treatment information that are spread over social media using Arabic language. In addition, it presents the process of creating a dataset that is called Health-Related Rumors Dataset (HRRD) which will be available and beneficial for further studies in health-related research. Furthermore, an experiment has been conducted to investigate the performance of several machine learning methods to detect the health-related rumors on social media for Arabic language. The experimental results showed the rumors can be detected with an accuracy of 83.50%.

Faisal Saeed, Wael M.S. Yafooz, Mohammed Al-Sarem and Essa Abdullah Hezzam, “Detecting Health-Related Rumors on Twitter using Machine Learning Methods” International Journal of Advanced Computer Science and Applications(IJACSA), 11(8), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110842

@article{Saeed2020,
title = {Detecting Health-Related Rumors on Twitter using Machine Learning Methods},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110842},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110842},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {8},
author = {Faisal Saeed and Wael M.S. Yafooz and Mohammed Al-Sarem and Essa Abdullah Hezzam}
}