X'ing submitted docs: Dos and Don'ts

Description

Improvements can be made to the structure and content of newly submitted documents shared to Twitter. Ideally, we would produce more compelling content on a per-document basis ('single-user mode') by leveraging the unique and valuable information only we have access to via Biofactoid. Ultimately, we'd like to achieve some positive feedback: increasing the chances of views, re-tweets, indexing (Google) prompts others to contribute.

Some Tweet "Dos and Don'ts" to use as a guide:

Dos

Tag
- With some prioritization and limits (number)
- Authors with unambiguous account names
- sourced from ORCID (#1017)
- Institutions, labs
Display a compelling image
Hashtag
- topics that are unambiguous, popular and useful
Text
- Narrative
- Summary
- GPT
- Semantic scholar TLDR feature
Links
- Directly to the paper
- via ID that can be normalized
  - including: DOI; PubMed ID; ISBN; arXiv IDs; ClinicalTrials.gov records
  - Altmetric > Sources of Attention
  - ~~CrossRef > Event Data~~ Deprecated

Don'ts

Split Tweet content into multiple posts e.g. thread
Tag/Hashtag
- Everything and everyone possible, as this detracts from content and looks like spam

Specification

To illustrate how we might apply these rules, I'll use a paper from eLife for which a hand-crafted Tweet was created. This paper is rather broad in scope (not just about an interaction), contains complex details, a broad range of experimental techniques and subtle conclusions.

Mockup

The mockup below is from a 'Features' series whose intention was to highlight the official recommendation by eLife in June.

Details

Some breakdown of the above example:

Tag
- journal directly (@eLife) - could be automated?
- Senior author (@@LoweLabMSKCC) - manual
Hastag
- main disease (#hepatocellular)
Link
- DOI, biofactoid doc in same tweet
Key image
- figure - manual

PathwayCommons / factoid