WeSeer: Visual Analysis for Better Information Cascade Prediction of WeChat Articles

Sexually transmitted diseases and immunology articles pushed by WeChat official accounts are of most interest to the Chinese public: A national cross-sectional study in China

Background

Social media has become a new channel for information exchange in recent years. WeChat official account (WOA) is now widely adopted by the Center for Disease Control and Prevention (CDC) for successful information distribution and diffusion online. We aimed to identify features of the most popular articles pushed by WOAs of the China's CDC that are associated with article influence.

Methods

The most popular articles per month between January 2017 and December 2019 were collected through the monthly published influence list of the CDC's WOA. Descriptive characteristics of articles were examined and the amount of reads and likes was converted into the WeChat communication index (WCI) which reflected the article's influence. Univariate and multiple linear regression analyses were applied to identify factors associated with article influence.

Results

A total of 720 most influential articles from 76 WOAs operated by 69 CDCs were selected for analysis. "Suzhou Vaccination", "China CDC News" and "Guangdong CDC" were the three top WOAs pushing the most quantities of popular articles. The average reads, likes and WCI of all articles were 43115.18, 355.82, and 4363.38, respectively. The article content, pushing year, and season were significantly associated with the article's influence. Sexually transmitted diseases [b = 207.57, 95 % confidence interval (CI): 82.23-332.91] and immunology (b = 123.16, 95 % CI: 39.65-206.67) articles obtained the highest impact.

Conclusion

Our findings are considered helpful for CDCs in improving and enhancing the article content that expands the coverage of health information dissemination. CDCs can make use of the WOAs for formulating, implementing, and refining plans pertinent to public health matters.

How Official Social Media Affected the Infodemic among Adults during the First Wave of COVID-19 in China

The COVID-19 pandemic has demonstrated that social media can impact society both positively (e.g., keeping citizens connected and informed) and negatively (e.g., the deliberate spreading of misinformation). This study aims to examine the underlying mechanisms of the relationship between official social media accounts and the infodemic, experienced during the first wave of COVID-19 in China. A theoretical model is proposed to examine how official social media accounts affected the infodemic during this period. In total, 1398 questionnaire responses were collected via WeChat and Tencent QQ, two leading Chinese social media platforms. Data analysis was conducted using Partial Lease Squares Structural Equation Modeling (PLS-SEM), moderation effect analysis, and mediation effect analysis. Results indicate that the Information Quality (IQ) of Official social media accounts (β = −0.294, p < 0.001) has a significant negative effect on the infodemic. Mediation effect analysis revealed that both social support (β = −0.333, 95% Boot CI (−0.388, −0.280)) and information cascades (β = −0.189, 95% Boot CI (−0.227, −0.151)) mediate the relationship between IQ and the infodemic. Moderation effect analysis shows that private social media usage (F = 85.637, p < 0.001) positively moderates the relationship between IQ and the infodemic, while health literacy has a small negative moderation effect on the relationship between IQ and the infodemic. Our findings show that, in the context of Chinese media, official social media accounts act as a major source of information for influencing the infodemic through increasing social support and reducing information cascades for citizens.

PolicyFlow

Stability in social, technical, and financial systems, as well as the capacity of organizations to work across borders, requires consistency in public policy across jurisdictions. The diffusion of laws and regulations across political boundaries can reduce the tension that arises between innovation and consistency. Policy diffusion has been a topic of focus across the social sciences for several decades, but due to limitations of data and computational capacity, researchers have not taken a comprehensive and data-intensive look at the aggregate, cross-policy patterns of diffusion. This work combines visual analytics and text and network analyses to help understand how policies, as represented in digitized text, spread across states. As a result, our approach can quickly guide analysts to progressively gain insights into policy adoption data. We evaluate the effectiveness of our system via case studies with a real-world policy dataset and qualitative interviews with domain experts.