The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
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Timeline
X Demographics
Mendeley readers
| Title |
COVID-Twitter-BERT: A natural language processing model to analyse COVID-19 content on Twitter
|
|---|---|
| Published in |
Frontiers in Artificial Intelligence, March 2023
|
| DOI | 10.3389/frai.2023.1023281 |
| Pubmed ID | |
| Authors |
Martin Müller, Marcel Salathé, Per E. Kummervold |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 17% |
| France | 1 | 8% |
| Switzerland | 1 | 8% |
| Unknown | 8 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 11 | 92% |
| Scientists | 1 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 268 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 268 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 33 | 12% |
| Student > Ph. D. Student | 32 | 12% |
| Student > Bachelor | 24 | 9% |
| Lecturer | 10 | 4% |
| Student > Doctoral Student | 10 | 4% |
| Other | 30 | 11% |
| Unknown | 129 | 48% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 79 | 29% |
| Social Sciences | 7 | 3% |
| Business, Management and Accounting | 6 | 2% |
| Medicine and Dentistry | 6 | 2% |
| Unspecified | 6 | 2% |
| Other | 28 | 10% |
| Unknown | 136 | 51% |