# Papers with Code

> website documenting machine learning research articles

**Wikidata**: [Q105775455](https://www.wikidata.org/wiki/Q105775455)  
**Source**: https://4ort.xyz/entity/papers-with-code

## Summary
Papers with Code is a website that documents machine learning research articles. It is accessible at https://paperswithcode.com/ and maintains an active presence on platforms including Twitter, GitHub, and Medium.

## Key Facts
- Papers with Code is an instance of a website.
- Primary use: machine learning.
- Website URL: https://paperswithcode.com/ (language of work or name: English).
- Common alias: PwC.
- Twitter handle: @paperswithcode (recorded start_time: 2018-12-22; qualifier value: 1076526138736951298; point_in_time qualifier: 2021-03-08).
- Recorded social media follower counts for the account include 49,722 (point_in_time: 2021-03-09), 82,054 (point_in_time: 2022-02-21), and 118,430 (preferred; point_in_time: 2023-02-04).
- GitHub username: paperswithcode.
- Medium username: paperswithcode.
- Wikidata description: "website documenting machine learning research articles."
- Main Wikidata property listed as P12490.

## FAQs
### Q: What is Papers with Code?
A: Papers with Code is a website that documents machine learning research articles and makes those articles available via its web domain at https://paperswithcode.com/.

### Q: What subject area does Papers with Code focus on?
A: The site is used for and documents work in machine learning.

### Q: Where can I follow or find community content for Papers with Code?
A: Papers with Code maintains social and community presences under the handle "paperswithcode" on Twitter, GitHub, and Medium.

### Q: How large is its social following?
A: Recorded follower counts on the associated social account include 49,722 (2021-03-09), 82,054 (2022-02-21), and 118,430 (preferred; 2023-02-04).

## Why It Matters
Papers with Code serves as a dedicated web resource that documents research in machine learning. By organizing and presenting machine-learning articles on a central website, it creates a discoverable point of reference for researchers, practitioners, and readers seeking literature in the field. Its presence across platforms such as Twitter, GitHub, and Medium indicates multiple channels for dissemination and engagement, and the documented growth in social followers reflects uptake and audience reach over time. As a named entity (also known by the alias PwC) with explicit identifiers on code and publishing platforms, Papers with Code connects written research with online community and code-hosting ecosystems, making it a notable node in the machine learning information landscape.

## Notable For
- Explicit focus on documenting machine learning research articles.
- Multi-platform presence: an official website plus accounts on Twitter, GitHub, and Medium under the username "paperswithcode."
- Measured social growth with recorded follower counts rising from 49,722 (2021-03-09) to 118,430 (preferred; 2023-02-04).
- Recognized on Wikidata with the description "website documenting machine learning research articles" and main property P12490.
- Commonly referred to by the alias "PwC."

## Body
### Overview
- Entity type: instance of a website.
- Short description (Wikidata): website documenting machine learning research articles.
- Primary topical focus: machine learning.

### Online presence and identifiers
- Official website: https://paperswithcode.com/ (language: English).
- Twitter: handle "paperswithcode" with a recorded start_time of 2018-12-22 and qualifier value 1076526138736951298 (point_in_time 2021-03-08).
- GitHub username: paperswithcode.
- Medium username: paperswithcode.
- Main Wikidata property associated: P12490.

### Social metrics (reported)
- Followers recorded as 49,722 (point_in_time 2021-03-09).
- Followers recorded as 82,054 (point_in_time 2022-02-21).
- Followers recorded as 118,430 (preferred; point_in_time 2023-02-04).

### Naming and aliases
- Alias listed: PwC.
- Language of work or name: English.

### Classification and metadata
- Classified as a website (a set of related web pages served from a single web domain).
- Wikidata description and structured properties identify its role in documenting machine learning research articles.