# Longitudinal Analysis of Discussion Topics in an Online Breast Cancer Community using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1603.08458 · HTML: https://mlsanity.com/paper/1603.08458/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2016.

## Built on by (most cited first, 1 of 1)

- [Multidimensional classification of posts for online course discussion forum curation](https://mlsanity.com/paper/2508.10008/) (2025)

## Abstract (arXiv)

Identifying topics of discussions in online health communities (OHC) is critical to various 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 a longitudinal analysis to show topic distributions and topic changes throughout members' participation. Our experimental results suggest that CNN outperforms other classifiers in the task of topic classification, and that certain trajectories can be detected with respect to topic changes.