Brown, D. C., & Al-Hossami, E., & Cheng, Z., & Alameda, A. L., & Johnson, T. N., & Uddin, M., & Janies, D. A. (2023, June), A network analysis of the Twitter-Rxiv ecosystem for purveyors of science misinformation in preprints on the COVID-19 pandemic  Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42441

\bibitem{asee_peer_42441}
  Brown, D. C., \& Al-Hossami, E., \& Cheng, Z., \& Alameda, A. L., \& Johnson, T. N., \& Uddin, M., \& Janies, D. A. (2023, June), \emph{A network analysis of the Twitter-Rxiv ecosystem for purveyors of science misinformation in preprints on the COVID-19 pandemic}
  Paper presented at 2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland.
  10.18260/1-2--42441

David C. Brown, Erfan Al-Hossami, Zhuo Cheng, Alyssa Lasmarias Alameda, Tia Nicole Johnson, Mesbah Uddin, and Daniel Andrew Janies.  "A network analysis of the Twitter-Rxiv ecosystem for purveyors of science misinformation in preprints on the COVID-19 pandemic".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://peer.asee.org/42441 Internet.  23 Apr, 2025

\bibitem{asee_peer_42441}
  David C. Brown, Erfan Al-Hossami, Zhuo Cheng, Alyssa Lasmarias Alameda, Tia Nicole Johnson, Mesbah Uddin, and Daniel Andrew Janies.
  "A network analysis of the Twitter-Rxiv ecosystem for purveyors of science misinformation in preprints on the COVID-19 pandemic".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://peer.asee.org/42441 Internet.  23 Apr, 2025

@INPROCEEDINGS{asee_peer_42441
author = "David C. Brown, Erfan Al-Hossami, Zhuo Cheng, Alyssa Lasmarias Alameda, Tia Nicole Johnson, Mesbah Uddin, and Daniel Andrew Janies"
title = "A network analysis of the Twitter-Rxiv ecosystem for purveyors of science misinformation in preprints on the COVID-19 pandemic"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/42441}
number = {10.18260/1-2--42441}
}

TY  - CPAPER
AB  - This paper illustrates the final research product resulting from a team of diverse students of many educational stages and backgrounds in cyber intelligence-based research.  We chose a real-world dataset of discussion of scientific preprints on SARS-CoV-2 virus and COVID disease on Twitter ™.  The selection of the real-world dataset was driven by: (a) misinformation regarding COVID-19 disease and SARS-CoV-2 virus is rampant and undermines our ability to recover from the pandemic, (b) unfounded and false health-related claims are spreading on social media, and (c) the rapid dissemination of health misinformation provides challenging competition with information broadcast by public health or government authorities such as the World Health Organization and the U.S. Centers for Disease Control and Prevention. Thus, we focused on the close symbiosis between preprints, preprint servers (like bioRxiv and medRxiv), Twitter, scientific researchers, journalists, and the public that developed in the early months of the pandemic (e.g. first six months of 2020). This symbiosis, the "Twitter-Rxiv ecosystem", led to the rapid dissemination of results before traditional processes of scientific peer-review before publication. While much of the work in preprints is well-intentioned, concerns have been raised that this symbiosis may be exploited to disseminate spurious results or intentionally incorrect information.  In response we constructed networks to represent public discourse surrounding scientific preprint literature on Twitter and develop metrics to score users within these networks. One such metric, peer-review percentage score, is useful for calculating the network prominence (i.e. influence) of a user while weighting that user for the quality of information propagated by the user. Peer-review percentage score can be used to identify subject-matter experts who transmit evidence-based information online. We found that these subject-matter experts outcompeted public health authorities in online forums by transmitting scientific results. Subject-matter experts engaged with the public whereas public health authorities did not. 
AU  - David C. Brown
AU  - Erfan Al-Hossami
AU  - Zhuo Cheng
AU  - Alyssa Lasmarias Alameda
AU  - Tia Nicole Johnson
AU  - Mesbah Uddin
AU  - Daniel Andrew Janies
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - A network analysis of the Twitter-Rxiv ecosystem for purveyors of science misinformation in preprints on the COVID-19 pandemic
UR  - https://peer.asee.org/42441
DO  - 10.18260/1-2--42441
ER  -