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

被引:10
|
作者
Binkheder, Samar [1 ]
Aldekhyyel, Raniah N. [1 ]
AlMogbel, Alanoud
Al-Twairesh, Nora [2 ,3 ]
Alhumaid, Nuha [4 ]
Aldekhyyel, Shahad N. [4 ]
Jamal, Amr A. [5 ,6 ]
机构
[1] King Saud Univ, Coll Med, Med Educ Dept, Med Informat & Elearning Unit, Riyadh 12372, Saudi Arabia
[2] King Saud Univ, Coll Comp & Informat Sci, Informat Technol Dept, Riyadh 12372, Saudi Arabia
[3] King Saud Univ, STCs Artificial Intelligence Chair, Riyadh 11451, Saudi Arabia
[4] King Saud bin Abdulaziz Univ Hlth Sci, Coll Publ Hlth & Hlth Informat, Riyadh 14611, Saudi Arabia
[5] King Saud Univ, Evidence Based Hlth Care & Knowledge Translat Res, Riyadh 11451, Saudi Arabia
[6] King Saud Univ, Coll Med, Family & Community Med Dept, Riyadh 12372, Saudi Arabia
关键词
COVID-19; coronavirus; social media; Twitter; mHealth applications; public health; sentiment analysis; network analysis; health informatics; TWITTER; TECHNOLOGY; CHILD; CARE;
D O I
10.3390/ijerph182413388
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
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页数:22
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