Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning

Quality of cerebral palsy videos on Chinese social media platforms

A significant research gap exists in evaluating the prevalence and quality of Chinese videos depicting CP on domestic social media platforms. In contrast to studies that focus on online video content concerning CP on YouTube, CP videos on YouTube are largely inaccessible to average citizens in mainland China. This disparity underscores the need for further investigation into the availability and nature of CP videos specifically on Chinese social media platforms. To assess the reliability and quality of short videos related to cerebral palsy (CP) on Chinese social media platforms. The present cross-sectional study examined 344 videos about CP from popular Chinese social media platforms, including TikTok, Kwai, Weibo, Bilibili, and RED. The analysis of these videos involved a detailed assessment of their sources, content, and characteristics. Additionally, quantitative scoring tools such as journal of the American medical association (JAMA) benchmarks, gobal quality score (GQS), and DISCERN were utilized to evaluate video quality. Furthermore, the potential relationship between video quality and various attributes such as duration, number of likes, and comments was explored and their impact on the quality of information presented in the videos was analyzed. The average duration of the 344 videos was 92.12 s (SD 105.69). CP rehabilitation training videos comprised 45.64% of the total, followed by expert-contributed videos at 40.70%. Mean scores for JAMA, GQS, and DISCERN were 1.62 (SD 0.60), 2.05 (SD 0.99), and 1.26 (SD 1.26) respectively. RED had the lowest average scores. Videos focusing on disease knowledge scored highest on JAMA and GQS scales. Experts achieved significantly higher GQS and DISCERN scores compared to health-related institutions and amateurs. Spearman correlation analysis revealed a strong positive correlation between likes and comments (r = .0.87, P < .0.001). Enhancing the management of medical content is crucial to address the compromised reliability of Chinese online short videos providing information to families of CP patients. Improving content professionalism and accuracy ensures users access genuinely valuable information.

Promoting Public Engagement in Palliative and End-of-Life Care Discussions on Chinese Social Media: Model Development and Analysis

BACKGROUND

In Chinese traditional culture, discussions surrounding death are often considered taboo, leading to a poor quality of death, and limited public awareness and knowledge about palliative and end-of-life care (PEoLC). However, the increasing prevalence of social media in health communication in China presents an opportunity to promote and educate the public about PEoLC through online discussions.

OBJECTIVE

This study aimed to examine the factors influencing public engagement in PEoLC discussions on a Chinese social media platform and develop practice recommendations to promote such engagement.

METHODS

We gathered 30,811 PEoLC-related posts on Weibo, the largest social media platform in China. Guided by the elaboration likelihood model, our study examined factors across 4 dimensions: content theme, mood, information richness, and source credibility. Content theme was examined using thematic analysis, while sentiment analysis was used to determine the mood of the posts. The impact of potential factors on post engagement was quantified using negative binomial regression.

RESULTS

Organizational accounts exhibited lower engagement compared to individual accounts (incidence rate ratio [IRR]<1; P<.001), suggesting an underuse of organizational accounts in advocating for PEoLC on Weibo. Posts centered on PEoLC-related entertainment (films, television shows, and books; IRR=1.37; P<.001) or controversial social news (IRR=1.64; P<.001) garnered more engagement, primarily published by individual accounts. An interaction effect was observed between content theme and post mood, with posts featuring more negative sentiment generally attracting higher public engagement, except for educational-related posts (IRR=2.68; P<.001).

CONCLUSIONS

Overall, organizations faced challenges in capturing public attention and involving the public when promoting PEoLC on Chinese social media platforms. It is imperative to move beyond a traditional mode to incorporate cultural elements of social media, such as engaging influencers, leveraging entertainment content and social news, or using visual elements, which can serve as effective catalysts in attracting public attention. The strategies developed in this study are particularly pertinent to nonprofit organizations and academics aiming to use social media for PEoLC campaigns, fundraising efforts, or research dissemination.

Disaster Health Care and Resiliency: A Systematic Review of the Application of Social Network Data Analytics

OBJECTIVES

This systematic literature review explores the applications of social network platforms for disaster health care management and resiliency and investigates their potential to enhance decision-making and policy formulation for public health authorities during such events.

METHODS

A comprehensive search across academic databases yielded 90 relevant studies. Utilizing qualitative and thematic analysis, the study identified the primary applications of social network data analytics during disasters, organizing them into 5 key themes: communication, information extraction, disaster Management, Situational Awareness, and Location Identification.

RESULTS

The findings highlight the potential of social networks as an additional tool to enhance decision-making and policymaking for public health authorities in disaster settings, providing a foundation for further research and innovative approaches in this field.

CONCLUSIONS

However, analyzing social network data has significant challenges due to the massive volume of information generated and the prevalence of misinformation. Moreover, it is important to point out that social network users do not represent individuals without access to technology, such as some elderly populations. Therefore, relying solely on social network data analytics is insufficient for effective disaster health care management. To ensure efficient disaster management and control, it is necessary to explore alternative sources of information and consider a comprehensive approach.

Impacts of government social media on public engagement in low-carbon practices focusing on Japan

This study investigates the role of Government Social Media (GSM) in enhancing public engagement with Low-Carbon Practices (LCP) in Japan. Motivated by the need to foster sustainable development and mitigate climate change impacts, this research utilizes negative binomial regression model analyzing 1022 posts from nine Japanese government social media accounts. Our findings reveal that increased media richness negatively correlates with engagement, suggesting that content depth over visual appeal is more effective for LCP-related communication. Surprisingly, the dialogic loop reduces engagement, indicating complex public reactions to governmental initiatives. Content themes related to governmental actions and LCP information significantly enhance engagement, while emotional valence shows minimal impact. The study introduces 'social media capital' as a moderating factor, which mitigates the negative effects of dialogic loops and media richness on engagement, and influences the impact of content themes. These insights provide a foundation for future research and guide the development of effective public engagement strategies in environmental policy. The study highlights the need for nuanced GSM strategies that prioritize information quality and relevance to increase public participation in low-carbon initiatives.

Engaging communities as partners in health crisis response: a realist-informed scoping review for research and policy

BACKGROUND

Health is increasingly affected by multiple types of crises. Community engagement is recognised as being a critical element in successful crisis response, and a number of conceptual frameworks and global guideline documents have been produced. However, little is known about the usefulness of such documents and whether they contain sufficient information to guide effective community engagement in crisis response. We undertake a scoping review to examine the usefulness of conceptual literature and official guidelines on community engagement in crisis response using a realist-informed analysis [exploring contexts, mechanisms, and outcomes(CMOs)]. Specifically, we assess the extent to which sufficient detail is provided on specific health crisis contexts, the range of mechanisms (actions) that are developed and employed to engage communities in crisis response and the outcomes achieved. We also consider the extent of analysis of interactions between the mechanisms and contexts which can explain whether successful outcomes are achieved or not.

SCOPE AND FINDINGS

We retained 30 documents from a total of 10,780 initially identified. Our analysis found that available evidence on context, mechanism and outcomes on community engagement in crisis response, or some of their elements, was promising, but few documents provided details on all three and even fewer were able to show evidence of the interactions between these categories, thus leaving gaps in understanding how to successfully engage communities in crisis response to secure impactful outcomes. There is evidence that involving community members in all the steps of response increases community resilience and helps to build trust. Consistent communication with the communities in time of crisis is the key for effective responses and helps to improve health indicators by avoiding preventable deaths.

CONCLUSIONS

Our analysis confirms the complexity of successful community engagement and the need for strategies that help to deal with this complexity to achieve good health outcomes. Further primary research is needed to answer questions of how and why specific mechanisms, in particular contexts, can lead to positive outcomes, including what works and what does not work and how to measure these processes.

Using Clinician-Patient WeChat Group Communication Data to Identify Symptom Burdens in Patients With Uterine Fibroids Under Focused Ultrasound Ablation Surgery Treatment: Qualitative Study

BACKGROUND

Unlike research project-based health data collection (questionnaires and interviews), social media platforms allow patients to freely discuss their health status and obtain peer support. Previous literature has pointed out that both public and private social platforms can serve as data sources for analysis.

OBJECTIVE

This study aimed to use natural language processing (NLP) techniques to identify concerns regarding the postoperative quality of life and symptom burdens in patients with uterine fibroids after focused ultrasound ablation surgery.

METHODS

Screenshots taken from clinician-patient WeChat groups were converted into free texts using image text recognition technology and used as the research object of this study. From 408 patients diagnosed with uterine fibroids in Chongqing Haifu Hospital between 2010 and 2020, we searched for symptom burdens in over 900,000 words of WeChat group chats. We first built a corpus of symptoms by manually coding 30% of the WeChat texts and then used regular expressions in Python to crawl symptom information from the remaining texts based on this corpus. We compared the results with a manual review (gold standard) of the same records. Finally, we analyzed the relationship between the population baseline data and conceptual symptoms; quantitative and qualitative results were examined.

RESULTS

A total of 408 patients with uterine fibroids were included in the study; 190,000 words of free text were obtained after data cleaning. The mean age of the patients was 39.94 (SD 6.81) years, and their mean BMI was 22.18 (SD 2.78) kg/m2. The median reporting times of the 7 major symptoms were 21, 26, 57, 2, 18, 30, and 49 days. Logistic regression models identified preoperative menstrual duration (odds ratio [OR] 1.14, 95% CI 5.86-6.37; P=.009), age of menophania (OR -1.02 , 95% CI 11.96-13.47; P=.03), and the number (OR 2.34, 95% CI 1.45-1.83; P=.04) and size of fibroids (OR 0.12, 95% CI 2.43-3.51; P=.04) as significant risk factors for postoperative symptoms.

CONCLUSIONS

Unstructured free texts from social media platforms extracted by NLP technology can be used for analysis. By extracting the conceptual information about patients' health-related quality of life, we can adopt personalized treatment for patients at different stages of recovery to improve their quality of life. Python-based text mining of free-text data can accurately extract symptom burden and save considerable time compared to manual review, maximizing the utility of the extant information in population-based electronic health records for comparative effectiveness research.

Improved healthcare disaster decision-making utilizing information extraction from complementary social media data during the COVID-19 pandemic

Managing an extreme event like a healthcare disaster requires accurate information about the event's circumstances to comprehend the full consequences of acting. However, information quality is rarely optimal since it takes time to determine the information of relevance. The COVID-19 pandemic showed that even official data sources are far from optimal since they suffer from reporting delays that slow decision-making. To support decision-makers with timely information, we utilize data from online social networks to propose an adaptable information extraction solution to create indices helping to forecast COVID-19 case numbers and hospitalization rates. We show that combining heterogeneous data sources like Twitter and Reddit can leverage these sources' inherent complementarity and yield better predictions than those using a single data source alone. We further show that the predictions run ahead of the official COVID-19 incidences by up to 14 days. Additionally, we highlight the importance of model adjustments whenever new information becomes available or the underlying data changes by observing distinct changes in the presence of specific symptoms on Reddit.

Timeliness of online COVID-19 reports from official sources

Introduction

Making epidemiological indicators for COVID-19 publicly available through websites and social media can support public health experts in the near-real-time monitoring of the situation worldwide, and in the establishment of rapid response and public health measures to reduce the consequences of the pandemic. Little is known, however, about the timeliness of such sources. Here, we assess the timeliness of official public COVID-19 sources for the WHO regions of Europe and Africa.

Methods

We monitored official websites and social media accounts for updates and calculated the time difference between daily updates on COVID-19 cases. We covered a time period of 52 days and a geographic range of 62 countries, 28 from the WHO African region and 34 from the WHO European region.

Results

The most prevalent categories were social media updates only (no website reporting) in the WHO African region (32.7% of the 1,092 entries), and updates in both social media and websites in the WHO European region (51.9% of the 884 entries for EU/EEA countries, and 73.3% of the 884 entries for non-EU/EEA countries), showing an overall clear tendency in using social media as an official source to report on COVID-19 indicators. We further show that the time difference for each source group and geographical region were statistically significant in all WHO regions, indicating a tendency to focus on one of the two sources instead of using both as complementary sources.

Discussion

Public health communication via social media platforms has numerous benefits, but it is worthwhile to do it in combination with other, more traditional means of communication, such as websites or offline communication.

Exploring the Impact of Online and Offline Channel Advantages on Brand Relationship Performance: The Mediating Role of Consumer Perceived Value

As omnichannel shopping behavior becomes increasingly popular among consumers, how to leverage the respective advantages and synergies of online and offline channels to retain customers for a long time is an urgent issue for retailers to solve. The purpose of this study is to explore the key advantages of online and offline channels influencing the omnichannel shopping experience in the decision-making process, and investigate their impact on consumer perceived value and brand relationship performance, as well as the interaction effect of online channel advantages and offline channel advantages. This study identifies the key advantages of online channels (search convenience, customer-generated information richness, and social connection) and offline channels (direct product experience, sales-staff assistance, and servicescape aesthetics) through a qualitative study and relevant literature review. Then, the proposed research framework was tested using the structural model equation in AMOS and hierarchical regression techniques in SPSS utilizing data from 347 shoppers. The results show that all variables except customer-generated information richness have positive impact on consumer perceived value. Other than search convenience and customer-generated information richness, consumer perceived value mediates the effect of other variables on brand relationship performance. Additionally, the interaction effect of online and offline channel advantages positively impacts consumer perceived value.

Using Clinician-Patient WeChat Group Communication Data to Identify Symptom Burdens in Patients With Uterine Fibroids Under Focused Ultrasound Ablation Surgery Treatment: Qualitative Study (Preprint).. [DOI: 10.2196/preprints.43995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Unlike research project–based health data collection (questionnaires and interviews), social media platforms allow patients to freely discuss their health status and obtain peer support. Previous literature has pointed out that both public and private social platforms can serve as data sources for analysis.

OBJECTIVE

This study aimed to use natural language processing (NLP) techniques to identify concerns regarding the postoperative quality of life and symptom burdens in patients with uterine fibroids after focused ultrasound ablation surgery.

METHODS

Screenshots taken from clinician-patient WeChat groups were converted into free texts using image text recognition technology and used as the research object of this study. From 408 patients diagnosed with uterine fibroids in Chongqing Haifu Hospital between 2010 and 2020, we searched for symptom burdens in over 900,000 words of WeChat group chats. We first built a corpus of symptoms by manually coding 30% of the WeChat texts and then used regular expressions in Python to crawl symptom information from the remaining texts based on this corpus. We compared the results with a manual review (gold standard) of the same records. Finally, we analyzed the relationship between the population baseline data and conceptual symptoms; quantitative and qualitative results were examined.

RESULTS

A total of 408 patients with uterine fibroids were included in the study; 190,000 words of free text were obtained after data cleaning. The mean age of the patients was 39.94 (SD 6.81) years, and their mean BMI was 22.18 (SD 2.78) kg/m². The median reporting times of the 7 major symptoms were 21, 26, 57, 2, 18, 30, and 49 days. Logistic regression models identified preoperative menstrual duration (odds ratio [OR] 1.14, 95% CI 5.86-6.37; <i>P</i>=.009), age of menophania (OR –1.02 , 95% CI 11.96-13.47; <i>P</i>=.03), and the number (OR 2.34, 95% CI 1.45-1.83; <i>P</i>=.04) and size of fibroids (OR 0.12, 95% CI 2.43-3.51; <i>P</i>=.04) as significant risk factors for postoperative symptoms.

CONCLUSIONS

Unstructured free texts from social media platforms extracted by NLP technology can be used for analysis. By extracting the conceptual information about patients’ health-related quality of life, we can adopt personalized treatment for patients at different stages of recovery to improve their quality of life. Python-based text mining of free-text data can accurately extract symptom burden and save considerable time compared to manual review, maximizing the utility of the extant information in population-based electronic health records for comparative effectiveness research.

MMKP: A mind mapping knowledgebase prototyping tool for precision medicine

Background

With significant advancements in the area of precision medicine, the breadth and complexity of the relevant knowledge in the field has increased significantly. However, the difficulty associated with dynamic modelling and the disorganization of such knowledge hinders its rapid development potential.

Results

To overcome the difficulty in using the relational database model for dynamic modelling, and to aid in the organization of precision medicine knowledge, we developed the Mind Mapping Knowledgebase Prototyping (MMKP) tool. The MMKP implements a novel design that we call a “polymorphic foreign key”, which allows the establishment of a logical linkage between a single table field and a record from any table. This design has advantages in supporting dynamic changes to the structural relationships in precision medicine knowledge. Knowledge stored in MMKP is presented as a mind map to facilitate human interaction. When using this tool, medical experts may curate the structure and content of the precision knowledge in a flow that is similar to the human thinking process.

Conclusions

The design of polymorphic foreign keys natively supports knowledge modelling in the form of mind mapping, which avoids the hard-coding of medical logic into a rigid database schema and significantly reduces the workload that is required for adapting a relational data model to future changes to the medical logic. The MMKP tool provides a graphical user interface for both data management and knowledgebase prototyping. It supports the flexible customization of the data field constraints and annotations. MMKP is available as open-source code on GitHub: https://github.com/ZjuLiangsl/mmkp.

Machine learning applications for COVID-19 outbreak management

Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.