Determining the Topic Evolution and Sentiment Polarity for Albinism in a Chinese Online Health Community: Machine Learning and Social Network Analysis

Bi, Qiqing; Shen, Lining; Zhang, Zhiguo; Wang, Shimin; Dai, Wei; Liu, Cui

doi:10.2196/17813

Cited by 21 publications

(14 citation statements)

References 65 publications

(66 reference statements)

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“…Online forest plot allows readers to examine the information of their own interest on the dashboard that could be useful with the zoom-in and zoom-out function superior to the traditionally statistic graphics as presented in the previous studies. [35][36][37]…”

Section: Strengths Of This Studymentioning

confidence: 99%

Using the forest plot to compare citation achievements in bibliographic and meta-analysis studies since 2011 using data on PubMed Central: A retrospective study

Chien

Tsai

et al. 2022

Medicine

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Background: We saw a steady increase in the number of bibliographic studies published over the years. The reason for this rise is attributed to the better accessibility of bibliographic data and software packages that specialize in bibliographic analyses. Any difference in citation achievements between bibliographic and meta-analysis studies observed so far need to be verified. In this study, we aimed to identify the frequently observed MeSH terms in these 2 types of study and investigate whether the highlighted MeSH terms are strongly associated with one of the study types.Methods: By searching the PubMed Central database, 5121 articles relevant to bibliometric and meta-analysis studies were downloaded since 2011. Social network analysis was applied to highlight the major MeSH terms of quantitative and statistical methods in these 2 types of studies. MeSH terms were then individually tested for any differences in event counts over the years between study types using odds of 95% confidence intervals for comparison.Results: In these 2 studies, we found that the most productive countries were the United States (19.9%), followed by the United Kingdom (8.8%) and China (8.7%); the most number of articles were published in PLoS One (2.9%), Stat Med (2.5%), and Res Synth (2.4%); and the most frequently observed MeSH terms were statistics and numerical data in bibliographic studies and methods in meta-analysis. Differences were found when compared to the event counts and the citation achievements in these 2 study types. Conclusion:The breakthrough was made by developing a dashboard using forest plots to display the difference in event counts. The visualization of the observed MeSH terms could be replicated for future academic pursuits and applications in other disciplines using the odds of 95% confidence intervals.

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Section: Strengths Of This Studymentioning

confidence: 99%

Using the forest plot to compare citation achievements in bibliographic and meta-analysis studies since 2011 using data on PubMed Central: A retrospective study

Chien

Tsai

et al. 2022

Medicine

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“…The precision rate reflects the ability of the classifier to determine the whole sample; the recall rate intuitively reflects the proportion of positive samples that are correctly identified; and the F1-score can be interpreted as a weighted average of precision and recall. As shown in Table 2, the Bi-LSTM classifier exhibited the best performance for testing emotional polarity for the remaining corpora [57].…”

Section: Emotional Polarity Analysismentioning

confidence: 93%

Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data (Preprint)

Shen¹,

Wen-li²,

Cao³

et al. 2021

Preprint

Self Cite

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BACKGROUND Wuhan, the epicenter of the COVID-19 outbreak, imposed citywide lockdown measures on 23 January 2020. Neighboring cities in the Hubei province followed suit with the government enforcing social distancing measures to restrict the spread of the disease throughout the province. Few studies have examined the emotional attitudes of citizens on social media platforms towards the imposed social distancing measures, and the factors that affected their emotions. OBJECTIVE The aim of this study is twofold. First, we detect the emotional attitudes of different groups of Sina Weibo users towards the social distancing measures imposed by the Peoples Government of Hubei Province. Second, we study the influencing factors of their emotions as well as the impact of imposed measures on users' emotions. METHODS Sina Weibo, one of China's largest social media platforms, was chosen as the primary data source. Bi-directional Long Short-Term Memory (Bi-LSTM) was used to analyze users' emotions, while logistic regression analysis was employed to explore the influence of independent variables on users' emotions, such as age and spatial locations. Further, the moderating effects of social distancing measures on the relationship between user characteristics and users' emotions are discussed through the interaction effects between the measures and independent variables. RESULTS We identified six topics of discussion, including delaying the resumption of work and school, travel restrictions, traffic restrictions, extending the Lunar New Year holiday, closing public spaces, and community containment. First, the main emotions shown by citizens were negative, including anger and fear. Women were observed to hold stronger and more positive emotions than men. Users located in the Hubei Province showed the highest amount of negative emotions in Mainland China. In addition, those who lived in emigrant provinces, or those with a high number of follows and / or posts, were inclined to express positive emotions. Older users and those with more fans were inclined to be neutral, while users in the Hubei Province, and with a longer registration time on the Sina Weibo platform, were more likely to express negative emotions. Further, different social distancing measures had unalike moderating effects on the relationship between user characteristics and users' emotions. CONCLUSIONS This study identified the emotional attitudes of Sina Weibo users towards the social distancing measures imposed by the Peoples Government of Hubei Province, and determined the characteristics that affect users' emotions, including the factor of social distancing measures. The results provide government agencies with better understanding of Internet users opinions on related events, and provide a reference point for social media platforms to push targeted content.

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“…The precision rate reflects the ability of the classifier to determine the whole sample; the recall rate intuitively reflects the proportion of positive samples that are correctly identified; and the F1-score can be interpreted as a weighted average of precision and recall. As shown in Table 2 , the Bi-LSTM classifier exhibited the best performance for testing emotional polarity for the remaining corpora [ 57 ]. Subsequently, the Bi-LSTM classifier was used to predict the emotional polarity of all corpora, and this process was performed using Python.…”

Section: Methodsmentioning

confidence: 99%

Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data

Shen¹,

Zhang²,

Cao³

et al. 2021

JMIR Med Inform

Self Cite

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Background Wuhan, China, the epicenter of the COVID-19 pandemic, imposed citywide lockdown measures on January 23, 2020. Neighboring cities in Hubei Province followed suit with the government enforcing social distancing measures to restrict the spread of the disease throughout the province. Few studies have examined the emotional attitudes of citizens as expressed on social media toward the imposed social distancing measures and the factors that affected their emotions. Objective The aim of this study was twofold. First, we aimed to detect the emotional attitudes of different groups of users on Sina Weibo toward the social distancing measures imposed by the People’s Government of Hubei Province. Second, the influencing factors of their emotions, as well as the impact of the imposed measures on users’ emotions, was studied. Methods Sina Weibo, one of China’s largest social media platforms, was chosen as the primary data source. The time span of selected data was from January 21, 2020, to March 24, 2020, while analysis was completed in late June 2020. Bi-directional long short-term memory (Bi-LSTM) was used to analyze users’ emotions, while logistic regression analysis was employed to explore the influence of explanatory variables on users’ emotions, such as age and spatial location. Further, the moderating effects of social distancing measures on the relationship between user characteristics and users’ emotions were assessed by observing the interaction effects between the measures and explanatory variables. Results Based on the 63,169 comments obtained, we identified six topics of discussion—(1) delaying the resumption of work and school, (2) travel restrictions, (3) traffic restrictions, (4) extending the Lunar New Year holiday, (5) closing public spaces, and (6) community containment. There was no multicollinearity in the data during statistical analysis; the Hosmer-Lemeshow goodness-of-fit was 0.24 (χ28=10.34, P>.24). The main emotions shown by citizens were negative, including anger and fear. Users located in Hubei Province showed the highest amount of negative emotions in Mainland China. There are statistically significant differences in the distribution of emotional polarity between social distancing measures (χ220=19,084.73, P<.001), as well as emotional polarity between genders (χ24=1784.59, P<.001) and emotional polarity between spatial locations (χ24=1659.67, P<.001). Compared with other types of social distancing measures, the measures of delaying the resumption of work and school or travel restrictions mainly had a positive moderating effect on public emotion, while traffic restrictions or community containment had a negative moderating effect on public emotion. Conclusions Findings provide a reference point for the adoption of epidemic prevention and control measures, and are considered helpful for government agencies to take timely actions to alleviate negative emotions during public health emergencies.

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Determining the Topic Evolution and Sentiment Polarity for Albinism in a Chinese Online Health Community: Machine Learning and Social Network Analysis

Cited by 21 publications

References 65 publications

Using the forest plot to compare citation achievements in bibliographic and meta-analysis studies since 2011 using data on PubMed Central: A retrospective study

Using the forest plot to compare citation achievements in bibliographic and meta-analysis studies since 2011 using data on PubMed Central: A retrospective study

Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data (Preprint)

Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data

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