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X Demographics
Mendeley readers
| Title |
Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis
|
|---|---|
| Published in |
Frontiers in Artificial Intelligence, July 2020
|
| DOI | 10.3389/frai.2020.00042 |
| Pubmed ID | |
| Authors |
Rania Albalawi, Tet Hin Yeap, Morad Benyoucef |
X Demographics
The data shown below were collected from the profiles of 8 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 | 25% |
| Australia | 1 | 13% |
| France | 1 | 13% |
| Switzerland | 1 | 13% |
| Unknown | 3 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 88% |
| Scientists | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 392 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 392 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 48 | 12% |
| Student > Ph. D. Student | 36 | 9% |
| Researcher | 24 | 6% |
| Student > Bachelor | 22 | 6% |
| Unspecified | 17 | 4% |
| Other | 62 | 16% |
| Unknown | 183 | 47% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 80 | 20% |
| Business, Management and Accounting | 23 | 6% |
| Unspecified | 17 | 4% |
| Engineering | 17 | 4% |
| Social Sciences | 14 | 4% |
| Other | 48 | 12% |
| Unknown | 193 | 49% |