# Kardashian Index

The Kardashian Index (K-Index), named after Kim Kardashian, is a measure of the discrepancy between a scientist's social media profile and publication record.[1] Proposed in 2014, the measure compares the number of followers a research scientist has on Twitter to the number of citations they have for their peer-reviewed work.

## Definition

The relationship between the number of Twitter followers (${\displaystyle F}$) and the number of citations (${\displaystyle C}$) is described as:

${\displaystyle F=43.3C^{0.32}}$

The Kardashian Index is thus calculated as:

${\displaystyle {\text{K-index}}={\frac {F(a)}{F(c)}}}$

where ${\displaystyle F(a)}$ is the actual number of Twitter followers of researcher ${\displaystyle X}$ and ${\displaystyle F(c)}$ is the number that researcher should have given their citations.

## Interpretation

A high K-index indicates an over-blown scientific fame while a low K-index suggests that a scientist is being undervalued. According to the author, researchers whose K-index > 5 can be considered 'Science Kardashians'.

## Criticism

The K-index suggests that the number of citations of a given scientist is comparable to his or her scientific value. This assumption has been criticized.[2][3]

On the other hand, the proposal of the K-Index in itself can be interpreted as a criticism to the assumption that scientists should have a social media impact at all while in reality social media footprint has no correlation at all to scientific quality or scientific impact.[4]

## References

1. ^ Hall, N (July 30, 2014). "The Kardashian index: a measure of discrepant social media profile for scientists" (PDF). Genome Biology. 15 (7): 424. doi:10.1186/s13059-014-0424-0. PMC 4165362. PMID 25315513. Retrieved August 15, 2014.
2. ^
3. ^ "Citations are not a measure of quality". Archived from the original on 2014-08-19.
4. ^ Houstein, Stefanie; Peters, Isabella; Sugimoto, Cassidy R.; Thelwall, Mike; Larivière, Vincent (April 2014). "Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature". Journal of the Association for Information Science and Technology. 65 (4): 656–669. arXiv:1308.1838. doi:10.1002/asi.23101.