An analysis of the user occupational class through Twitter content
- Submitting institution
-
The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2452
- Type
- E - Conference contribution
- DOI
-
10.3115/v1/p15-1169
- Title of conference / published proceedings
- Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- First page
- 1754
- Volume
- 1: Long papers
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- May
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
D - Natural Language Processing
- Citation count
- 50
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first paper to demonstrate that NLP techniques can be employed to predict user occupational class from social media posts. Published at the top NLP conference, it led to three further papers (PLOS ONE; doi.org/10.1371/journal.pone.0138717, ECIR 2016; doi.org/10.1007/978-3-319-30671-1_54, ACM HyperText 2018; doi.org/10.1145/3209542.3209577, over 480 citations combined) as well as work by other groups e.g. Hasanuzzaman et al. (ACL, 2017) and Pan et al. (ACL, 2019). The paper led to six invited talks including Imperial College, Amazon, Warwick, and Cambridge University.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -