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

• Published in: International journal of environmental research and public health Vol. 18; no. 24; p. 13388
• Main Authors: Binkheder, Samar, Aldekhyyel, Raniah N, AlMogbel, Alanoud, Al-Twairesh, Nora, Alhumaid, Nuha, Aldekhyyel, Shahad N, Jamal, Amr A
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 20-12-2021; MDPI
• Subjects: Applications programs; Coronaviruses; COVID-19; Humans; Pandemics; Public Opinion; SARS-CoV-2; Saudi Arabia - epidemiology; Sentiment Analysis; Social Media; Social networks; Support vector machines; Telemedicine; User experience; Saudi Arabia; COVID-19; sentiment analysis; mHealth applications; Twitter; coronavirus; health informatics; network analysis; social media; public health
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph182413388

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.