| Title |
A universal information theoretic approach to the identification of stopwords
|
|---|---|
| Published in |
Nature Machine Intelligence, December 2019
|
| DOI | 10.1038/s42256-019-0112-6 |
| Authors |
Martin Gerlach, Hanyu Shi, Luís A. Nunes Amaral |
X Demographics
The data shown below were collected from the profiles of 50 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 17 | 34% |
| Austria | 2 | 4% |
| United Kingdom | 2 | 4% |
| Germany | 2 | 4% |
| France | 1 | 2% |
| Italy | 1 | 2% |
| India | 1 | 2% |
| Spain | 1 | 2% |
| Poland | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 20 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 30 | 60% |
| Members of the public | 17 | 34% |
| Science communicators (journalists, bloggers, editors) | 2 | 4% |
| Practitioners (doctors, other healthcare professionals) | 1 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 67 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 31% |
| Researcher | 8 | 12% |
| Student > Master | 7 | 10% |
| Lecturer | 4 | 6% |
| Professor | 2 | 3% |
| Other | 9 | 13% |
| Unknown | 16 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 21 | 31% |
| Engineering | 5 | 7% |
| Social Sciences | 5 | 7% |
| Physics and Astronomy | 3 | 4% |
| Neuroscience | 2 | 3% |
| Other | 10 | 15% |
| Unknown | 21 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 51. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 April 2022.
All research outputs
#810,364
of 24,998,746 outputs
Outputs from Nature Machine Intelligence
#232
of 715 outputs
Outputs of similar age
#19,496
of 471,890 outputs
Outputs of similar age from Nature Machine Intelligence
#9
of 28 outputs
Altmetric has tracked 24,998,746 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 715 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 65.1. This one has gotten more attention than average, scoring higher than 67% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 471,890 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.