| Title |
Interactive topic modeling
|
|---|---|
| Published in |
Machine Learning, October 2013
|
| DOI | 10.1007/s10994-013-5413-0 |
| Authors |
Yuening Hu, Jordan Boyd-Graber, Brianna Satinoff, Alison Smith |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 564 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 10 | 2% |
| Japan | 6 | 1% |
| China | 5 | <1% |
| United Kingdom | 4 | <1% |
| Germany | 2 | <1% |
| Chile | 1 | <1% |
| France | 1 | <1% |
| Brazil | 1 | <1% |
| Austria | 1 | <1% |
| Other | 5 | <1% |
| Unknown | 528 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 145 | 26% |
| Student > Master | 91 | 16% |
| Researcher | 71 | 13% |
| Student > Bachelor | 33 | 6% |
| Other | 31 | 5% |
| Other | 98 | 17% |
| Unknown | 95 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 285 | 51% |
| Social Sciences | 32 | 6% |
| Engineering | 29 | 5% |
| Business, Management and Accounting | 22 | 4% |
| Arts and Humanities | 15 | 3% |
| Other | 77 | 14% |
| Unknown | 104 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 25 October 2022.
All research outputs
#5,393,063
of 25,837,817 outputs
Outputs from Machine Learning
#155
of 1,259 outputs
Outputs of similar age
#46,203
of 226,383 outputs
Outputs of similar age from Machine Learning
#2
of 11 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,259 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done well, scoring higher than 87% 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 226,383 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.