- "Characterizing social media metrics of scholarly papers: The effect of document properties and collaboration patterns"
- "Disciplinary differences in Twitter scholarly communication"
- "How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications"
- "A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks"

Literature on Research on Twitter

Even prior to the COVID-19 pandemic, researchers used Twitter as a platform for knowledge sharing, the dissemination of scientific literature, research collaboration, and academic discussion.

University of Michigan - Ann Arbor

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Twitter is a social media platform that has also become a major source of information for many people, partly due the popularity among many politicians and celebrities.  

Knowledge sharing on Twitter

Twitter has been a main source of misinformation, especially during the COVID-19 pandemic. False information regarding ways to prevent the spread of the virus, the origins of the virus, side effects of the vaccine, and etc can all be found on Twitter. 

COVID-19 Misinformation on Twitter

Articles referenced on Twitter may affect the number of citations on the articles references. 

Citations of References on Twitter

Twitter has been used for academic discussions and research related to COVID-19. 

COVID-19 Research on Twitter

Since the initial developmental stages of the COVID-19 vaccines, Twitter has been a platform where people discussed and shared their thoughts and information about the vaccines. Some examples include public announcements such as the content of the vaccine and regional distributions and user expressing their perceptions and sentiments towards the vaccine. 

COVID-19 Vaccine Discussions on Twitter

Soft moderation of misinformation on Twitter involves interventions that provide contextual information or warnings rather than outright removing content. This includes strategies such as pairing a tweet containing misinformation with a corrective tweet containing factual information, or applying warning labels. These approaches act as soft nudges to inform users about potential falsehoods in the tweets they read.

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