What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer

• Published in: JMIR medical informatics Vol. 5; no. 3; p. e23
• Main Authors: Tapi Nzali, Mike Donald, Bringay, Sandra, Lavergne, Christian, Mollevi, Caroline, Opitz, Thomas
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 31.07.2017
• Subjects: Automation; Bioengineering; Breast cancer; Cancer therapies; Clinical trials; Computer Science; Datasets; Disease; Document and Text Processing; Life Sciences; Original Paper; Patients; Quality of life; Questionnaires; Semantic analysis; Semantics; Social and Information Networks; Social networks; breast cancer; text mining; social media; unsupervised learning
• ISSN: 2291-9694; 2291-9694
• DOI: 10.2196/medinform.7779

Social media dedicated to health are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of retrieving clinically relevant information from such social media in order to analyze the quality of life of patients with breast cancer. Our aim was to detect the different topics discussed by patients on social media and to relate them to functional and symptomatic dimensions assessed in the internationally standardized self-administered questionnaires used in cancer clinical trials (European Organization for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire Core 30 [QLQ-C30] and breast cancer module [QLQ-BR23]). First, we applied a classic text mining technique, latent Dirichlet allocation (LDA), to detect the different topics discussed on social media dealing with breast cancer. We applied the LDA model to 2 datasets composed of messages extracted from public Facebook groups and from a public health forum (cancerdusein.org, a French breast cancer forum) with relevant preprocessing. Second, we applied a customized Jaccard coefficient to automatically compute similarity distance between the topics detected with LDA and the questions in the self-administered questionnaires used to study quality of life. Among the 23 topics present in the self-administered questionnaires, 22 matched with the topics discussed by patients on social media. Interestingly, these topics corresponded to 95% (22/23) of the forum and 86% (20/23) of the Facebook group topics. These figures underline that topics related to quality of life are an important concern for patients. However, 5 social media topics had no corresponding topic in the questionnaires, which do not cover all of the patients' concerns. Of these 5 topics, 2 could potentially be used in the questionnaires, and these 2 topics corresponded to a total of 3.10% (523/16,868) of topics in the cancerdusein.org corpus and 4.30% (3014/70,092) of the Facebook corpus. We found a good correspondence between detected topics on social media and topics covered by the self-administered questionnaires, which substantiates the sound construction of such questionnaires. We detected new emerging topics from social media that can be used to complete current self-administered questionnaires. Moreover, we confirmed that social media mining is an important source of information for complementary analysis of quality of life.