A Novel Machine Learning Framework for Comparison of Viral COVID-19–Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis

Abstract

Background Social media plays a critical role in health communications, especially during global health emergencies such as the current COVID-19 pandemic. However, there is a lack of a universal analytical framework to extract, quantify, and compare content features in public discourse of emerging health issues on different social media platforms across a broad sociocultural spectrum. Objective We aimed to develop a novel and universal content feature extraction and analytical framework and contrast how content features differ with sociocultural background in discussions of the emerging COVID-19 global health crisis on major social media platforms. Methods We sampled the 1000 most shared viral Twitter and Sina Weibo posts regarding COVID-19, developed a comprehensive coding scheme to identify 77 potential features across six major categories (eg, clinical and epidemiological, countermeasures, politics and policy, responses), quantified feature values (0 or 1, indicating whether or not the content feature is mentioned in the post) in each viral post across social media platforms, and performed subsequent comparative analyses. Machine learning dimension reduction and clustering analysis were then applied to harness the power of social media data and provide more unbiased characterization of web-based health communications. Results There were substantially different distributions, prevalence, and associations of content features in public discourse about the COVID-19 pandemic on the two social media platforms. Weibo users were more likely to focus on the disease itself and health aspects, while Twitter users engaged more about policy, politics, and other societal issues. Conclusions We extracted a rich set of content features from social media data to accurately characterize public discourse related to COVID-19 in different sociocultural backgrounds. In addition, this universal framework can be adopted to analyze social media discussions of other emerging health issues beyond the COVID-19 pandemic.

The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study

“…Topic modeling, on the other hand, is an unsupervised clustering method. Among topic modeling studies, latent Dirichlet allocation (LDA) was the most widely used algorithm [ 12 , 15 , 17 , 19 , 21 , 22 , 24 ], and other favorites included k-means clustering [ 14 , 16 ]. For example, Chandrasekaran et al [ 15 ] utilized LDA to extract 26 topics among 13.9 million English COVID-19 Twitter posts.…”

“…Kwok et al [ 21 ] employed LDA to extract topics and Stanford University’s CoreNLP (natural language processing) to study the sentiments of Twitter posts regarding COVID-19 vaccinations from Australian Twitter accounts. Also, Chen et al [ 16 ] compared the COVID-19 discussions on Twitter and Weibo using t-distributed stochastic neighbor embedding dimensionality reduction with the k-means clustering algorithm to extract topics.…”

“…Machine learning and deep learning techniques have been employed to study COVID-19 posts on social media, with much of the focus on topic modeling, sentiment analysis, and misinformation detection [12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Both sentiment analysis and misinformation detection are supervised classification problems.…”

“…Despite the instructive knowledge provided by the aforementioned machine learning studies, there are two identifiable gaps. First, most studies concentrated on public social media platforms, with the majority using Twitter as the data source [12][13][14][15][16][17][18][19]21,22,24,25]. Investigations on closed social media platforms, such as WhatsApp, WeChat, Telegram, or LINE, remain extremely scarce.…”

Exploring Feasibility of Multivariate Deep Learning Models in Predicting COVID-19 Epidemic

“…More importantly, the multivariate LSTM developed in this study can be easily extended to further incorporate more data such as in the MIDAS GitHub repository (13). Other non-traditional data, such as social media, sensor-based data, and drone imaging, can be incorporated into the LSTM model to better characterize multiple aspects of COVID-19 dynamics(14)(15)(16)(17)(18)(19). Decentralized blockchain techniques and robotics could also provide rich and secure inputs for data-driven models such as LSTM…”