Project FAIRSciSocial
Transforming science social media activity into meaningful scientific contributions
Social media posts are a unique and valuable source of scientific knowledge, but they get buried in noisy feeds and locked away by platforms.
To truly harness the potential of science social media, we need these data to comply to the standards we expect of any scientific knowledge. Specifically, we are advocating for science social media that is open and FAIR: Findable, Accessible, Interoperable and Reusable.
FAIRSciSocial leverages an AI-powered assistant, TweetFAIRy, to enable researchers to easily “FAIRify” their social media feeds by detecting and converting research-related posts into FAIR nanopublications. In this way, researchers can get proper recognition for their science social media contributions, and those contributions can continue benefiting the science ecosystem in myriad forms, long after they fade out of ephemeral Twitter feeds.
How does it work?
Joining the FAIRSciSocial campaign is easy! Any researcher with a Twitter (X) social media account can join the FAIRSciSocial campaign through the TweetFAIRy app.*
- Sign in to the app with your Twitter account (optionally link your ORCID account as well to increase discoverability)
- Choose an automation setting to determine how involved you want to be in the FAIRification process:
- Automatic: posts or threads related to research will be automatically nanopublished (see below for more details on how we detect research-related posts)
- Supervised: posts will only be nanopublished after your manual review, in which you can additionally modify and annotate your post (see below for more details on post semantics).
- You’ll receive notifications whenever a post is nanopublished (for the automatic setting) or detected as a potential nanopublication for review (in the supervised setting)
* If you’re on a different social media platform, let us know and join the waitlist here!
FAQs
Is science social media really Science?
Do I have to be a professional academic/industry researcher to join?
Why are we representing science social media posts with Nanopublications?
Why is FAIR a relevant standard for science social media?
Even if we agree that science social media is a valuable part of the scientific record, another important question is how best to represent it as such. Specifically, science social media poses a number of significant challenges for data collection and modelling efforts:
- Scale: Handling the vast amount of data generated on science social media
- Diversity: Accounting for the wide range of formats and kinds of knowledge expressed in science social media posts. For example, posts can be links to articles,annotations of external content, or more complex discourse moves (e.g., expressing support/opposition to claims).
- Fragmentation: Dealing with the dispersal of relevant posts across various platforms
The FAIR data principles were designed for exactly those kinds of challenges. To quote from the official FAIR webpage:
The FAIR principles emphasize machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data
While we see the FAIR standards as particularly relevant for science social media, there are additional important standards guiding the development of scholarly infrastructure, such as detailed in The Principles of Open Scholarly Infrastructure (POSI).
What exciting apps are enabled by FAIR science social media?
FAIR science social media data would unlock myriad application possiblities.
- Citeable tweets
- Science feeds
- Integration across different social media networks
- Transcluded social media to comments section for any research webpage (journals, preprints, etc)
- Next generation AI + Semantic Data queries