Paper
26 June 2024 Towards a deeper understanding of social media behavior: patterns and trends
Mohamed Basel Almourad, Mohammed Hussein, Emad Bataineh, Zelal Wattar
Author Affiliations +
Proceedings Volume 13188, International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors (MIEITS 2024); 131880D (2024) https://doi.org/10.1117/12.3030117
Event: International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors (MIEITS 2024), 2024, Kuala Lumpur, Malaysia
Abstract
In many studies addressing smartphone usage, reliance on self-reported data, typically collected through questionnaires, has been commonplace. However, these investigations often offered a broad overview of overall smartphone usage without delving into specific app categories. This study, in contrast, employed a dataset derived from a smartphone application that objectively recorded user activities, encompassing details such as accessed apps and the initiation and termination times of each app session. Our analysis focused on discerning patterns of social media engagement within the subset of SPACE app utilized. The inferential analysis utilized the Mann–Whitney U test. Notably, the findings unveiled that youngsters exhibit a higher smartphone usage duration compared to grownups. Additionally, a gender-based disparity was observed, with females spending more time on social media than their male counterparts. Furthermore, females demonstrated a higher likelihood of initiating social media apps in comparison to males. This research, grounded in objective data, provides a nuanced understanding of social media engagement.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Mohamed Basel Almourad, Mohammed Hussein, Emad Bataineh, and Zelal Wattar "Towards a deeper understanding of social media behavior: patterns and trends", Proc. SPIE 13188, International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors (MIEITS 2024), 131880D (26 June 2024); https://doi.org/10.1117/12.3030117
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KEYWORDS
Web 2.0 technologies

Analytical research

Data processing

Statistical analysis

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