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Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia

Author

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  • Samar Binkheder

    (Medical Informatics and E-learning Unit, Medical Education Department, College of Medicine, King Saud University, Riyadh 12372, Saudi Arabia
    Samar Binkheder and Raniah N. Aldekhyyel contributed equally to this work.)

  • Raniah N. Aldekhyyel

    (Medical Informatics and E-learning Unit, Medical Education Department, College of Medicine, King Saud University, Riyadh 12372, Saudi Arabia
    Samar Binkheder and Raniah N. Aldekhyyel contributed equally to this work.)

  • Alanoud AlMogbel

    (Freelance Research Assistant, Riyadh 12372, Saudi Arabia)

  • Nora Al-Twairesh

    (Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia
    STC’s Artificial Intelligence Chair, King Saud University, Riyadh 11451, Saudi Arabia)

  • Nuha Alhumaid

    (College of Public Health and Health Informatics, King Saud bin Abdulaziz University for Health Sciences, Riyadh 14611, Saudi Arabia)

  • Shahad N. Aldekhyyel

    (College of Public Health and Health Informatics, King Saud bin Abdulaziz University for Health Sciences, Riyadh 14611, Saudi Arabia)

  • Amr A. Jamal

    (Evidence-Based Health Care & Knowledge Translation Research Chair, King Saud University, Riyadh 11451, Saudi Arabia
    Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh 12372, Saudi Arabia)

Abstract

A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were “Tawakkalna” followed by “Tabaud”, and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps’ services and user experience, especially during health crises.

Suggested Citation

  • Samar Binkheder & Raniah N. Aldekhyyel & Alanoud AlMogbel & Nora Al-Twairesh & Nuha Alhumaid & Shahad N. Aldekhyyel & Amr A. Jamal, 2021. "Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia," IJERPH, MDPI, vol. 18(24), pages 1-22, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:24:p:13388-:d:706241
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    References listed on IDEAS

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    Cited by:

    1. Munshi Muhammad Abdul Kader Jilani & Md. Moniruzzaman & Mouri Dey & Edris Alam & Md. Aftab Uddin, 2022. "Strengthening the Trialability for the Intention to Use of mHealth Apps Amidst Pandemic: A Cross-Sectional Study," IJERPH, MDPI, vol. 19(5), pages 1-16, February.
    2. Abdennour Boulesnane & Souham Meshoul & Khaoula Aouissi, 2022. "Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural Network," Mathematics, MDPI, vol. 10(21), pages 1-22, November.
    3. Chi-Jui Tsai & Wen-Jye Shyr, 2022. "Key Factors for Evaluating Visual Perception Responses to Social Media Video Communication," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    4. Mashael Alghareeb & Abdulmohsen Saud Albesher & Amna Asif, 2023. "Studying Users’ Perceptions of COVID-19 Mobile Applications in Saudi Arabia," Sustainability, MDPI, vol. 15(2), pages 1-17, January.

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