Abstract: In social networking platforms, comprehending vi-rality, exemplified by YouTube, is of great importance, which helps in understanding what characteristics utilised to create content along with what dynamics involved in contributing to YouTube’s strength as a platform for sharing content. The current literature surrounding virality problem appears sparse concern-ing development theories, investigations regarding empirical facts, and an understanding of what makes videos go viral. The over-arching objective is to understand deeply the phenomena of viral YouTube videos in Saudi Arabia, hence we propose an intelligent convergent parallel mixed-methods approach that begins, as an internal step, by a qualitative thematic analyses method and an NLP-based quantitative method independently, followed by training an unsupervised clustering model for integrating the internal analysis outputs for deeper insights. We have empirically analysed some trended YouTube videos along with their contents, for studying such phenomena. One of our main findings revealed that boosting entertainments, traditions, politics, and/or religion issues when making a video, that is associated in somehow with sarcastic or rude remarks, is likely the preeminent impulse for letting a regular video go viral.

Abdullah Alshanqiti, Ayman Bajnaid, Abdul Rehman Gilal, Shuaa Aljasir, Aeshah Alsughayyir and Sami Albouq, “Intelligent Parallel Mixed Method Approach for Characterising Viral YouTube Videos in Saudi Arabia” International Journal of Advanced Computer Science and Applications(IJACSA), 11(3), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110382

@article{Alshanqiti2020,
title = {Intelligent Parallel Mixed Method Approach for Characterising Viral YouTube Videos in Saudi Arabia},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110382},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110382},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {3},
author = {Abdullah Alshanqiti and Ayman Bajnaid and Abdul Rehman Gilal and Shuaa Aljasir and Aeshah Alsughayyir and Sami Albouq}
}