Social Media and Research Publication Activity During Early Stages of the COVID-19 Pandemic: Longitudinal Trend Analysis

Abstract

Background: The COVID-19 pandemic has highlighted the importance of rapid dissemination of scientific and medical discoveries. Current platforms available for the distribution of scientific and clinical research data and information include preprint repositories and traditional peer-reviewed journals. In recent times, social media has emerged as a helpful platform to share scientific and medical discoveries.

Objective: This study aimed to comparatively analyze activity on social media (specifically, Twitter) and that related to publications in the form of preprint and peer-reviewed journal articles in the context of COVID-19 and gastroenterology during the early stages of the COVID-19 pandemic.

Methods: COVID-19-related data from Twitter (tweets and user data) and articles published in preprint servers (bioRxiv and medRxiv) as well as in the PubMed database were collected and analyzed during the first 6 months of the pandemic, from December 2019 through May 2020. Global and regional geographic and gastrointestinal organ-specific social media trends were compared to preprint and publication activity. Any relationship between Twitter activity and preprint articles published and that between Twitter activity and PubMed articles published overall, by organ system, and by geographic location were identified using Spearman's rank-order correlation.

Results: Over the 6-month period, 73,079 tweets from 44,609 users, 7164 journal publications, and 4702 preprint publications were retrieved. Twitter activity (ie, number of tweets) peaked in March 2020, whereas preprint and publication activity (ie, number of articles published) peaked in April 2020. Overall, strong correlations were identified between trends in Twitter activity and preprint and publication activity (P<.001 for both). COVID-19 data across the three platforms mainly concentrated on pulmonology or critical care, but when analyzing the field of gastroenterology specifically, most tweets pertained to pancreatology, most publications focused on hepatology, and most preprints covered hepatology and luminal gastroenterology. Furthermore, there were significant positive associations between trends in Twitter and publication activity for all gastroenterology topics (luminal gastroenterology: P=.009; hepatology and inflammatory bowel disease: P=.006; gastrointestinal endoscopy: P=.007), except pancreatology (P=.20), suggesting that Twitter activity did not correlate with publication activity for this topic. Finally, Twitter activity was the highest in the United States (7331 tweets), whereas PubMed activity was the highest in China (1768 publications).

Conclusions: The COVID-19 pandemic has highlighted the potential of social media as a vehicle for disseminating scientific information during a public health crisis. Sharing and spreading information on COVID-19 in a timely manner during the pandemic has been paramount; this was achieved at a much faster pace on social media, particularly on Twitter. Future investigation could demonstrate how social media can be used to augment and promote scholarly activity, especially as the world begins to increasingly rely on digital or virtual platforms. Scientists and clinicians should consider the use of social media in augmenting public awareness regarding their scholarly pursuits.

Keywords: COVID-19; SARS-CoV-2; Twitter; coronavirus; dissemination; gastroenterology; literature; preprint; research; social media.

Figure 1

Study flow diagram outlining the data extraction method from 3 media platforms analyzed (Twitter, PubMed-NCBI, and medRxiv or bioRxiv). *Each month of the specific study period was split into half-month intervals for the purposes of analysis. **Duplicate publications from separate searches were individually reviewed and reorganized into the most appropriate subject group in order to eliminate potential for publications to be accounted for more than once. ***A follow-up review of preprint articles pertaining to COVID-19 and gastroenterology that ultimately resulted in formal peer-reviewed journal publications was performed for July 2020. IBD: inflammatory bowel disease; GI: gastrointestinal.

Figure 2

Trend of COVID-19–related tweets, ratio of impressions to tweets, publications and preprints. Twitter activity from December 1, 2019, through May 31, 2020, captured at half-month intervals by using the Twitter analytics platform Symplur Signals. Preprints from medRxiv and bioRxiv repositories were also abstracted during this time period along with publications indexed in the PubMed-NCBI database.

Figure 3

Trend of COVID-19 Twitter impressions. Number of impressions generated from Twitter from December 1, 2019, through May 31, 2020, captured at half-month intervals by using the Twitter analytics platform Symplur Signals.

Figure 4

Trend of Twitter, publication, and preprint activity related to COVID-19 and pulmonary or critical care topics. Comparison of the number of tweets posted with articles published in peer-reviewed journals and preprints published pertaining to COVID 19 and pulmonary or critical care at half-month intervals between December 2019 and May 2020.

Figure 5

Trend of COVID-19 and gastroenterology subspecialty–related Twitter, publication, and preprint activity. Trend in the number of (A) tweets, (B) publications, and (C) preprints published pertaining to COVID 19 and gastroenterology subspecialty topics at half-month intervals between December 2019 and May 2020. GI: gastrointestinal; IBD: inflammatory bowel disease.

Figure 6

Heat maps illustrating the total number of COVID-19–related tweets (A) across the globe and (C) in the United States, as well as publications indexed in the PubMed-NCBI represented (B) across the globe and (D) in the United States, over the 6-month study period (December 2019 through May 2020). Numbers are represented on the spectrum from the least (yellow) to the highest amount (maroon), as detailed in the legend accompanying each map. Countries or US states shaded white indicate the absence of data for those regions.