The data shown below were collected from the profiles of 27 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Use of Machine Learning to Detect Wildlife Product Promotion and Sales on Twitter
|
|---|---|
| Published in |
Frontiers in Big Data, August 2019
|
| DOI | 10.3389/fdata.2019.00028 |
| Pubmed ID | |
| Authors |
Qing Xu, Jiawei Li, Mingxiang Cai, Tim K. Mackey |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 22% |
| United Kingdom | 2 | 7% |
| Switzerland | 2 | 7% |
| Kenya | 1 | 4% |
| Comoros | 1 | 4% |
| Greece | 1 | 4% |
| Unknown | 14 | 52% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 24 | 89% |
| Scientists | 3 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 61 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 7 | 11% |
| Researcher | 6 | 10% |
| Student > Ph. D. Student | 6 | 10% |
| Lecturer | 3 | 5% |
| Unspecified | 3 | 5% |
| Other | 7 | 11% |
| Unknown | 29 | 48% |
| Readers by discipline | Count | As % |
|---|---|---|
| Environmental Science | 6 | 10% |
| Computer Science | 6 | 10% |
| Social Sciences | 5 | 8% |
| Agricultural and Biological Sciences | 4 | 7% |
| Unspecified | 3 | 5% |
| Other | 8 | 13% |
| Unknown | 29 | 48% |
Attention Score in Context
This research output has an Altmetric Attention Score of 37. 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 03 October 2022.
All research outputs
#1,150,275
of 26,374,136 outputs
Outputs from Frontiers in Big Data
#14
of 502 outputs
Outputs of similar age
#23,301
of 353,293 outputs
Outputs of similar age from Frontiers in Big Data
#3
of 10 outputs
Altmetric has tracked 26,374,136 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 502 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.8. This one has done particularly well, scoring higher than 97% 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 353,293 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 93% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.