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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Detecting causality from online psychiatric texts using inter-sentential language patterns
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, July 2012
|
| DOI | 10.1186/1472-6947-12-72 |
| Pubmed ID | |
| Authors |
Jheng-Long Wu, Liang-Chih Yu, Pei-Chann Chang |
| Abstract |
Online psychiatric texts are natural language texts expressing depressive problems, published by Internet users via community-based web services such as web forums, message boards and blogs. Understanding the cause-effect relations embedded in these psychiatric texts can provide insight into the authors' problems, thus increasing the effectiveness of online psychiatric services. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 2 | 67% |
| India | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 66 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 23% |
| Student > Ph. D. Student | 13 | 20% |
| Student > Master | 11 | 17% |
| Student > Bachelor | 3 | 5% |
| Professor | 3 | 5% |
| Other | 8 | 12% |
| Unknown | 13 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 18% |
| Psychology | 11 | 17% |
| Medicine and Dentistry | 9 | 14% |
| Nursing and Health Professions | 4 | 6% |
| Engineering | 3 | 5% |
| Other | 10 | 15% |
| Unknown | 17 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 23 July 2012.
All research outputs
#14,147,730
of 22,671,366 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,101
of 1,978 outputs
Outputs of similar age
#95,739
of 163,884 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#29
of 45 outputs
Altmetric has tracked 22,671,366 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,978 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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 163,884 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.