The Use of Traditional, Complementary, and Integrative Medicine in Cancer: Data-Mining Study of 1 Million Web-Based Posts From Health Forums and Social Media Platforms

Abstract

Background: Patients with cancer are increasingly using forums and social media platforms to access health information and share their experiences, particularly in the use of traditional, complementary, and integrative medicine (TCIM). Despite the popularity of TCIM among patients with cancer, few related studies have used data from these web-based sources to explore the use of TCIM among patients with cancer.

Objective: This study leveraged multiple forums and social media platforms to explore patients' use, interest, and perception of TCIM for cancer care.

Methods: Posts (in English) related to TCIM were collected from Facebook, Twitter, Reddit, and 16 health forums from inception until February 2022. Both manual assessments and natural language processing were performed. Descriptive analyses were performed to explore the most commonly discussed TCIM modalities for each symptom and cancer type. Sentiment analyses were performed to measure the polarity of each post or comment, and themes were identified from posts with positive and negative sentiments. TCIM modalities that are emerging or recommended in the guidelines were identified a priori. Exploratory topic-modeling analyses with latent Dirichlet allocation were conducted to investigate the patients' perceptions of these modalities.

Results: Among the 1,620,755 posts available, cancer-related symptoms, such as pain (10/10, 100% cancer types), anxiety and depression (9/10, 90%), and poor sleep (9/10, 90%), were commonly discussed. Cannabis was among the most frequently discussed TCIM modalities for pain in 7 (70%) out of 10 cancer types, as well as nausea and vomiting, loss of appetite, anxiety and depression, and poor sleep. A total of 7 positive and 7 negative themes were also identified. The positive themes included TCIM, making symptoms manageable, and reducing the need for medication and their side effects. The belief that TCIM and conventional treatments were not mutually exclusive and intolerance to conventional treatment may facilitate TCIM use. Conversely, TCIM was viewed as leading to patients' refusal of conventional treatment or delays in diagnosis and treatment. Doctors' ignorance regarding TCIM and the lack of information provided about TCIM may be barriers to its use. Exploratory analyses showed that TCIM recommendations were well discussed among patients; however, these modalities were also used for many other indications. Other notable topics included concerns about the legalization of cannabis, acupressure techniques, and positive experiences of meditation.

Conclusions: Using machine learning techniques, social media and health forums provide a valuable resource for patient-generated data regarding the pattern of use and patients' perceptions of TCIM. Such information will help clarify patients' needs and concerns and provide directions for research on integrating TCIM into cancer care. Our results also suggest that effective communication about TCIM should be achieved and that doctors should be more open-minded to actively discuss TCIM use with their patients.

Keywords: TCIM; cancer; cancer care; complementary; forums, digital health; integrative; machine learning; perceptions; social media; traditional; traditional, complementary, and integrative medicine.

Figure 1

Workflow of analysis of posts from health forums and social media. Texts were preprocessed (lemmatization, tokenization, and stemming) to clean and prepare text data. Sentiment analysis was performed to score the sentiment of each post from −1 to 1 (a negative score refers to negative sentiment and a positive score refers to positive sentiment). For semisupervised latent Dirichlet allocation (LDA), traditional, complementary, and integrative medicine (TCIM) modalities were grouped according to the classification by the National Centre for Complementary and Integrative Health. Gensim LDA algorithms were used in both semisupervised and unsupervised LDA to categorize texts into different numbers of topics. Word clouds were generated from topic modeling to visualize the top keywords from each topic, and domain knowledge was used to explore themes related to each topic. TF-IDF: term frequency–inverse document frequency. Tables S2 and S3 are from Multimedia Appendix 1.