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Disruptions in the Cystic Fibrosis Community's Experiences and Concerns During the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments.
Yao, Lean Franzl; Ferawati, Kiki; Liew, Kongmeng; Wakamiya, Shoko; Aramaki, Eiji.
  • Yao LF; Social Computing Laboratory, Nara Institute of Science and Technology, Ikoma, Japan.
  • Ferawati K; Social Computing Laboratory, Nara Institute of Science and Technology, Ikoma, Japan.
  • Liew K; Social Computing Laboratory, Nara Institute of Science and Technology, Ikoma, Japan.
  • Wakamiya S; Social Computing Laboratory, Nara Institute of Science and Technology, Ikoma, Japan.
  • Aramaki E; Social Computing Laboratory, Nara Institute of Science and Technology, Ikoma, Japan.
J Med Internet Res ; 25: e45249, 2023 04 20.
Article in English | MEDLINE | ID: covidwho-2306090
ABSTRACT

BACKGROUND:

The COVID-19 pandemic disrupted the needs and concerns of the cystic fibrosis community. Patients with cystic fibrosis were particularly vulnerable during the pandemic due to overlapping symptoms in addition to the challenges patients with rare diseases face, such as the need for constant medical aid and limited information regarding their disease or treatments. Even before the pandemic, patients vocalized these concerns on social media platforms like Reddit and formed communities and networks to share insight and information. This data can be used as a quick and efficient source of information about the experiences and concerns of patients with cystic fibrosis in contrast to traditional survey- or clinical-based methods.

OBJECTIVE:

This study applies topic modeling and time series analysis to identify the disruption caused by the COVID-19 pandemic and its impact on the cystic fibrosis community's experiences and concerns. This study illustrates the utility of social media data in gaining insight into the experiences and concerns of patients with rare diseases.

METHODS:

We collected comments from the subreddit r/CysticFibrosis to represent the experiences and concerns of the cystic fibrosis community. The comments were preprocessed before being used to train the BERTopic model to assign each comment to a topic. The number of comments and active users for each data set was aggregated monthly per topic and then fitted with an autoregressive integrated moving average (ARIMA) model to study the trends in activity. To verify the disruption in trends during the COVID-19 pandemic, we assigned a dummy variable in the model where a value of "1" was assigned to months in 2020 and "0" otherwise and tested for its statistical significance.

RESULTS:

A total of 120,738 comments from 5827 users were collected from March 24, 2011, until August 31, 2022. We found 22 topics representing the cystic fibrosis community's experiences and concerns. Our time series analysis showed that for 9 topics, the COVID-19 pandemic was a statistically significant event that disrupted the trends in user activity. Of the 9 topics, only 1 showed significantly increased activity during this period, while the other 8 showed decreased activity. This mixture of increased and decreased activity for these topics indicates a shift in attention or focus on discussion topics during this period.

CONCLUSIONS:

There was a disruption in the experiences and concerns the cystic fibrosis community faced during the COVID-19 pandemic. By studying social media data, we were able to quickly and efficiently study the impact on the lived experiences and daily struggles of patients with cystic fibrosis. This study shows how social media data can be used as an alternative source of information to gain insight into the needs of patients with rare diseases and how external factors disrupt them.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Cystic Fibrosis / Social Media / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Qualitative research Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2023 Document Type: Article Affiliation country: 45249

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Cystic Fibrosis / Social Media / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Qualitative research Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2023 Document Type: Article Affiliation country: 45249