# Breaking Community Boundary: Comparing Academic and Social Communication Preferences regarding Global Pandemics

arXiv: https://arxiv.org/abs/2104.05409 · HTML: https://mlsanity.com/paper/2104.05409/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 69 references that are in the map.
- First posted on arXiv 12 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [Do altmetrics correlate with citations? Extensive comparison of altmetric indicators with citations from a multidisciplinary perspective](https://mlsanity.com/paper/1401.4321/) (2014)
- [Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set](https://mlsanity.com/paper/2003.07372/) (2020)
- [Could scientists use Altmetric.com scores to predict longer term citation counts?](https://mlsanity.com/paper/1801.10311/) (2018)
- [CoronaVis: A Real-time COVID-19 Tweets Data Analyzer and Data Repository](https://mlsanity.com/paper/2004.13932/) (2020)
- [Data mining and analysis of scientific research data records on Covid 19 mortality, immunity, and vaccine development in the first wave of the Covid 19 pandemic](https://mlsanity.com/paper/2009.05793/) (2020)
- [Alternative metrics in scientometrics: A meta-analysis of research into three altmetrics](https://mlsanity.com/paper/1407.8010/) (2014)
- [Tracking Public Opinion in China through Various Stages of the COVID-19 Pandemic](https://mlsanity.com/paper/2006.00163/) (2020)

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

- [Reliable Decision Support with LLMs: A Framework for Evaluating Consistency in Binary Text Classification Applications](https://mlsanity.com/paper/2505.14918/) (2025)

## Abstract (arXiv)

The global spread of COVID-19 has caused pandemics to be widely discussed. This is evident in the large number of scientific articles and the amount of user-generated content on social media. This paper aims to compare academic communication and social communication about the pandemic from the perspective of communication preference differences. It aims to provide information for the ongoing research on global pandemics, thereby eliminating knowledge barriers and information inequalities between the academic and the social communities. First, we collected the full text and the metadata of pandemic-related articles and Twitter data mentioning the articles. Second, we extracted and analyzed the topics and sentiment tendencies of the articles and related tweets. Finally, we conducted pandemic-related differential analysis on the academic community and the social community. We mined the resulting data to generate pandemic communication preferences (e.g., information needs, attitude tendencies) of researchers and the public, respectively. The research results from 50,338 articles and 927,266 corresponding tweets mentioning the articles revealed communication differences about global pandemics between the academic and the social communities regarding the consistency of research recognition and the preferences for particular research topics. The analysis of large-scale pandemic-related tweets also confirmed the communication preference differences between the two communities.