Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks.
Identifying topics of discussions in online health communities (OHC) is critical to various information extraction applications, but can be difficult because topics of OHC content are usually heterogeneous and domain-dependent. In this paper, we provide a multi-class schema, an annotated dataset, and supervised classifiers based on convolutional neural network (CNN) and other models for the task of classifying discussion topics. We apply the CNN classifier to the most popular breast cancer online community, and carry out cross-sectional and longitudinal analyses to show topic distributions and topic dynamics throughout members' participation. Our experimental results suggest that CNN outperforms other classifiers in the task of topic classification and identify several patterns and trajectories. For example, although members discuss mainly disease-related topics, their interest may change through time and vary with their disease severities.
Department of Biomedical Informatics, Columbia University, New York, NY, USA. Electronic address: sz2338@columbia.edu.
Shaodian
S
Zhang
Department of Biomedical Informatics, Columbia University, New York, NY, USA. Electronic address: edouard.grave@gmail.com.
Edouard
E
Grave
King's College London, London, UK. Electronic address: elizabeth.sklar@kcl.ac.uk.
Elizabeth
E
Sklar
Department of Biomedical Informatics, Columbia University, New York, NY, USA. Electronic address: noemie.elhadad@columbia.edu.
Noémie
N
Elhadad
D001943:Breast Neoplasms; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D020407:Internet; D016571:Neural Networks, Computer; D010358:Patient Participation
Breast cancer; Convolutional neural network; Deep learning; Longitudinal analysis; Online health community; Topic