Cancer Prevention and Treatment on Chinese Social Media:Machine Learning-Based Content Analysis Study

Background: Nowadays, social media plays a crucial role in disseminating information about cancer prevention and treatment.A growing body of research has focused on assessing access and communication effects of cancer information on social media.However, there remains a limited understanding of the comprehensive presentation of cancer prevention and treatment methodsacross social media platforms. Furthermore, research comparing the differences between medical social media (MSM) andcommon social media (CSM) is also lacking. Objective: Using big data analytics, this study aims to comprehensively map the characteristics of cancer treatment and preventioninformation on MSM and CSM. This approach promises to enhance cancer coverage and assist patients in making informedtreatment decisions. Methods: We collected all posts (N=60,843) from 4 medical WeChat official accounts (accounts with professional medicalbackgrounds, classified as MSM in this paper) and 5 health and lifestyle WeChat official accounts (accounts with nonprofessionalmedical backgrounds, classified as CSM in this paper). We applied latent Dirichlet allocation topic modeling to extract cancer-relatedposts (N=8427) and identified 6 cancer themes separately in CSM and MSM. After manually labeling posts according to ourcodebook, we used a neural-based method for automated labeling. Specifically, we framed our task as a multilabel task andutilized different pretrained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Global Vectorsfor Word Representation (GloVe), to learn document-level semantic representations for labeling. Results: We analyzed a total of 4479 articles from MSM and 3948 articles from CSM related to cancer. Among these, 35.52%(2993/8427) contained prevention information and 44.43% (3744/8427) contained treatment information. Themes in CSM werepredominantly related to lifestyle, whereas MSM focused more on medical aspects. The most frequently mentioned preventionmeasures were early screening and testing, healthy diet, and physical exercise. MSM mentioned vaccinations for cancer preventionmore frequently compared with CSM. Both types of media provided limited coverage of radiation prevention (including sunprotection) and breastfeeding. The most mentioned treatment measures were surgery, chemotherapy, and radiotherapy. Comparedwith MSM (1137/8427, 13.49%), CSM (2993/8427, 35.52%) focused more on prevention. Conclusions: The information about cancer prevention and treatment on social media revealed a lack of balance. The focuswas primarily limited to a few aspects, indicating a need for broader coverage of prevention measures and treatments in socialmedia. Additionally, the study's findings underscored the potential of applying machine learning to content analysis as a promisingresearch approach for mapping key dimensions of cancer information on social media. These findings hold methodological andpractical significance for future studies and health promotion