| Title |
Extracting health-related causality from twitter messages using natural language processing
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, April 2019
|
| DOI | 10.1186/s12911-019-0785-0 |
| Pubmed ID | |
| Authors |
Son Doan, Elly W. Yang, Sameer S. Tilak, Peter W. Li, Daniel S. Zisook, Manabu Torii |
X Demographics
The data shown below were collected from the profiles of 5 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 Kingdom | 1 | 20% |
| Germany | 1 | 20% |
| United States | 1 | 20% |
| France | 1 | 20% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 108 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 13 | 12% |
| Student > Master | 12 | 11% |
| Student > Ph. D. Student | 12 | 11% |
| Researcher | 8 | 7% |
| Student > Doctoral Student | 7 | 6% |
| Other | 13 | 12% |
| Unknown | 43 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 22 | 20% |
| Medicine and Dentistry | 7 | 6% |
| Engineering | 4 | 4% |
| Nursing and Health Professions | 4 | 4% |
| Psychology | 3 | 3% |
| Other | 18 | 17% |
| Unknown | 50 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 May 2021.
All research outputs
#13,407,546
of 23,140,503 outputs
Outputs from BMC Medical Informatics and Decision Making
#963
of 2,016 outputs
Outputs of similar age
#172,761
of 351,526 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#20
of 46 outputs
Altmetric has tracked 23,140,503 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,016 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 50% 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 351,526 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 46 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 56% of its contemporaries.