Page 172 - GHES-3-2

Global Health Economics and
Sustainability
Online health community reviews

Liu, J., & Kong, J. (2021). Why do users of online mental health fever health information seeking behaviors in online health
communities get likes and reposts: A combination of text community using a mixed-methods approach. Digital
mining and empirical analysis. Healthcare (Basel), 9(9):1133. Health, 10:1-15.
https://doi.org/10.3390/healthcare9091133 https://doi.org/10.1177/20552076241282622
Liu, J., Kong, J., & Zhang, X. (2020). Study on differences between Rustam, F., Khalid, M., Aslam, W., Rupapara, V., Mehmood, A., &
patients with physiological and psychological diseases in Choi, G.S. (2021). A performance comparison of supervised
online health communities: Topic analysis and sentiment machine learning models for Covid-19 tweets sentiment
analysis. International Journal of Environmental Research analysis. PLos One, 16(2):e0245909.
and Public Health, 17(5):1508.
https://doi.org/10.1371/journal.pone.0245909
https://doi.org/10.3390/ijerph17051508
Shen, L., Yao, R., Zhang, W., Evans, R., Cao, G., & Zhang, Z.
Lu, Y., Wu, Y., Liu, J., Li, J., & Zhang, P. (2017). Understanding (2021). Emotional attitudes of chinese citizens on social
health care social media use from different stakeholder distancing during the COVID-19 outbreak: Analysis of
perspectives: A content analysis of an online health social media data. JMIR Medical Informatics, 9(3):e27079.
community. Journal of Medical Internet Research, 19(4):e109.
https://doi.org/10.2196/27079
https://doi.org/10.2196/jmir.7087
SnowNLP. (2017). SnowNLP: Simplified Chinese Text Processing.
Luo, A., Xin, Z., Yuan, Y., Wen, T., Xie, W., Zhong, Z., et al. https://github.com/isnowfy/snownlp [Last accessed on
(2020). Multidimensional feature classification of the health 2025 Jan 05].
information needs of patients with hypertension in an Sun, Y., Yu, J., Chiu, Y.L., & Hsu, Y.T. (2022). Can online health
online health community through analysis of 1000 patient information sources really improve patient satisfaction?
question records: Observational study. Journal of Medical Frontiers in Public Health, 10:940800.
Internet Research, 22(5):e17349.
https://doi.org/10.3389/fpubh.2022.940800
https://doi.org/10.2196/17349
Wu, J., Hou, S.X., Jin, M.M., & Hou, Z.Y. (2017). LDA feature
Nandwani, P., & Verma, R. (2021) A review on sentiment analysis selection based text classification and user clustering in
and emotion detection from text. In: Social Network Chinese online health community. Journal of China Society
Analysis and Mining. Vol. 11. Germany: Springer, p.81.
for Scientific and Technical Information, 36(11):1183-1191.
https://doi.org/10.1007/s13278-021-00776-6
Xiang, M., Zhong, D., Han, M., & Lv, K. (2023). A study on online
Necaise, A., & Amon, M.J. (2024). Peer support for chronic pain health community users’ information demands based on the
in online health communities: Quantitative study on the BERT-LDA model. Healthcare (Basel), 11(15):2142.
dynamics of social interactions in a chronic pain forum. https://doi.org/10.3390/healthcare11152142
Journal of Medical Internet Research, 26:e45858.
Zeng, J.F., Ma, X., & Zhou, K. (2019). Enhancing attention-based
https://doi.org/10.2196/45858
LSTM with position context for aspect-level sentiment
Ortigosa, A., Martín, José M., & Carro, R.M. (2014). Sentiment classification. IEEE Access, 7:20462-20471.
analysis in facebook and its application to e-learning. https://doi.org/10.1109/ACCESS.2019.2893806
Computers in Human Behavior, 31:527-541.
Zhang, S., Bantum, E., Owen, J., & Elhadad, N. (2014). Does
https://doi.org/10.1016/j.chb.2013.05.024
sustained participation in an online health community
Pan, X., Tang, Z., Liu, Y., & Ren, J. (2024). Analysis on childhood affect sentiment? AMIA Symposium, 2014:1970-1979.

Volume 3 Issue 2 (2025) 164 https://doi.org/10.36922/ghes.7052