Analysis of Caregiver Burden Expressed in Social Media Discussions

Abstract

Almost 40% of US adults provide informal caregiving, yet research gaps remain around what burdens affect informal caregivers. This study uses a novel social media site, Reddit, to mine and better understand what online communities focus on as their caregiving burdens. These forums were accessed using an application programming interface, a machine learning classifier was developed to remove low information posts, and topic modeling was applied to the corpus. An expert panel summarized the forums' themes into ten categories. The largest theme extracted from Reddit's forums discussed the personal emotional toll of being a caregiver. This was followed by logistic issues while caregiving and caring for parents who have cancer. Smaller themes included approaches to end-of-life care, physical equipment needs when caregiving, and the use of wearables or technology to help monitor care recipients. The platform often discusses caregiving for parents which may reflect the age of Reddit's users. This study confirms that Reddit forums are used for caregivers to discuss the burdens associated with their role and the types of stress that can result from informal caregiving.

Keywords: caregiver burden; social media data mining; spillover effects.

Figure A1

t—SNE plot of topic clusters for interactive viewing of posts. Each dot represents a Reddit submission post. Clustering or shapes represents cohesiveness in the body of the text. Colors represent topic themes (1–10). Hovering over a dot provides the associated text in the Reddit submission post allowing for quick ingestion of complex themes.

Figure A2

PyLDAvis interactive viz showing term frequency within corpus and topics. Topic themes are represented as circles number 1—10 and clicking on the numbered circle provides the most common terms within the topic cluster. Adjusting the lambda bar in the upper right corner providers the reviewer with the most common words in the cluster and the most common words in the cluster that are not found in the corpus [54,55].

Figure 1

Methodology flowchart from accessing Reddit posts through creation of Latent Dirichlet Allocation for two corpuses (full Reddit dataset and Submissions-only dataset).

Figure 2

Coherence plots for determining LDA topic size hyperparameter in the Full Reddit dataset (Full) and the Submissions-only dataset (Submissions).

Figure 3

Topics over time showing only 2017 through 2021, although the corpus included low-level response in years prior to 2017. The yellow bar shows when COVID-19 was named by the World Health Organization.