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Background: Nowadays, new media has played an important role in providing information about cancer prevention and treatment. A growing body of work has been devoted to examining the access and communication effects of cancer information on social media. However, there has been limited understanding of the overall presentation of cancer prevention and treatment on social media. Further, research on comparing the differences between medical social media and common social media remained limited.Objective: Based on big data analytics, this study aimed to comprehensively map the characteristics of cancer treatment and prevention information on medical social media and common social media, which was promisingly helpful in cancer coverage and patients' treatment decision. Methods:We collected all posts (N=60,843) from 4 medical WeChat official accounts (classified as medical social media in this paper), and 5 health and lifestyle WeChat official accounts (classified as common social media in this paper). By applying latent Dirichlet allocation topic model, we extracted cancer-related posts (N=8,427) and obtained 6 cancer themes in common social media and medical social media separately. After manually labeling posts according to our codebook, we adopted a neural-based method to label different articles automatically. To be more specific, we defined our task as a multi-label task and chose different pre-trained models, say, Bert and Glove, to learn document level semantic representations for labelling. Results:Themes in common social media were more related to lifestyle, while medical social media were more related to medical attributions. Early screening and testing, healthy diet, and physical exercise were the most frequently mentioned preventive measures. Compared with common social media, medical social media mentioned vaccinations to prevent cancer more frequently. Both types of media provided limited coverage of radiation prevention (including sun protection) and breastfeeding. Surgery, chemotherapy, and radiotherapy were the most mentioned treatment measures. Medical social media discussed treatment information more than common social media. Conclusions:Cancer prevention and treatment information on social media revealed a lack of balance. The focus on cancer prevention and treatment information was mainly limited to a few aspects. The cancer coverage on preventive measures and treatments in social media required further improvement. Additionally, the study's findings underscored the potential of applying machine learning to content analysis as a promising research paradigm for mapping the key dimensions of cancer information on social media. The findings provided methodological and practical significance in future study and health promotion.
Background: Nowadays, new media has played an important role in providing information about cancer prevention and treatment. A growing body of work has been devoted to examining the access and communication effects of cancer information on social media. However, there has been limited understanding of the overall presentation of cancer prevention and treatment on social media. Further, research on comparing the differences between medical social media and common social media remained limited.Objective: Based on big data analytics, this study aimed to comprehensively map the characteristics of cancer treatment and prevention information on medical social media and common social media, which was promisingly helpful in cancer coverage and patients' treatment decision. Methods:We collected all posts (N=60,843) from 4 medical WeChat official accounts (classified as medical social media in this paper), and 5 health and lifestyle WeChat official accounts (classified as common social media in this paper). By applying latent Dirichlet allocation topic model, we extracted cancer-related posts (N=8,427) and obtained 6 cancer themes in common social media and medical social media separately. After manually labeling posts according to our codebook, we adopted a neural-based method to label different articles automatically. To be more specific, we defined our task as a multi-label task and chose different pre-trained models, say, Bert and Glove, to learn document level semantic representations for labelling. Results:Themes in common social media were more related to lifestyle, while medical social media were more related to medical attributions. Early screening and testing, healthy diet, and physical exercise were the most frequently mentioned preventive measures. Compared with common social media, medical social media mentioned vaccinations to prevent cancer more frequently. Both types of media provided limited coverage of radiation prevention (including sun protection) and breastfeeding. Surgery, chemotherapy, and radiotherapy were the most mentioned treatment measures. Medical social media discussed treatment information more than common social media. Conclusions:Cancer prevention and treatment information on social media revealed a lack of balance. The focus on cancer prevention and treatment information was mainly limited to a few aspects. The cancer coverage on preventive measures and treatments in social media required further improvement. Additionally, the study's findings underscored the potential of applying machine learning to content analysis as a promising research paradigm for mapping the key dimensions of cancer information on social media. The findings provided methodological and practical significance in future study and health promotion.
BACKGROUND The incidence of liver cancer is increasing in China, which poses a serious threat to human health. WeChat, as one of the primary platforms for Chinese people to obtain health information, plays an important role in providing a large amount of medical information. However, no one has evaluated the quality of articles about liver cancer on WeChat. This work evaluates the quality of health information on liver cancer in WeChat platform, to benefit the patients while making treatment decisions and provide suggestions on publishing high-quality treatment health information. OBJECTIVE Our study aimed to assess the quality of the information in articles which possess great views on WeChat associated with liver cancer. METHODS In September 2023, with “liver cancer” as keywords, searches were implemented on the WeChat according to inclusion and exclusion criteria. And the DISCERN instrument (DISCERN) was applied to perform the assessment. RESULTS A total of 95 articles with more than 100,000 views were included and analyzed. Commercial organizations uploaded the largest percentage of articles, accounting for 40% (38), and lifestyle intervention was mentioned most among the articles, accounting for 48% (46). The assessment based on the DISCERN instrument reported a mean score of 26.88 (SD 44.28). 77.7% (73) of the articles were considered to have a very poor quality, while 21.2 % (21) of them were considered to be of poor quality and only 1.1% (1) of them were fair. There were no articles evaluated as being of good or excellent quality. The quality of articles with references is better than articles without references (P <.001). However, there were no significant differences in the quality of articles from different uploading sources and information categories. Also, the presence of pictures, videos, and advertisements did not seem to have a significant effect on article quality. CONCLUSIONS The quality of articles on health information about liver cancer that are widely read on WeChat is poor while article authors should focus more on improving the quality of information. The reliability of articles can be improved by providing more details about treatment options, or by citing professional guidelines or references, thus helping people make the best decision when retrieving health information.
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