Unemployment detection on Twitter

Data and models for ACL 2022 paper "Multilingual Detection of Personal Employment Status on Twitter"

Data

An anonymized version of the dataset can be found on 🤗 under this link.

Models

Our model have been open-sourced on 🤗 and can be found in this collection. Please find in the table below the model names in the hub for each language and class.

Class	English	Spanish	Portuguese
Is Unemployed	worldbank/bert-twitter-en-is-unemployed	worldbank/bert-twitter-es-is-unemployed	worldbank/bert-twitter-pt-is-unemployed
Lost Job	worldbank/bert-twitter-en-lost-job	worldbank/bert-twitter-es-lost-job	worldbank/bert-twitter-pt-lost-job
Job Search	worldbank/bert-twitter-en-job-search	worldbank/bert-twitter-es-job-search	worldbank/bert-twitter-pt-job-search
Is Hired	worldbank/bert-twitter-en-is-hired	worldbank/bert-twitter-es-is-hired	worldbank/bert-twitter-pt-is-hired
Job Offer	worldbank/bert-twitter-en-job-offer	worldbank/bert-twitter-es-job-offer	worldbank/bert-twitter-pt-job-offer

To use a specific model, say the English model for the class Is Unemployed, do the following:

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("worldbank/bert-twitter-en-is-unemployed")
model = AutoModel.from_pretrained("worldbank/bert-twitter-en-is-unemployed")

Citation

If you find our work useful, please cite:

@inproceedings{tonneau-etal-2022-multilingual,
    title = "Multilingual Detection of Personal Employment Status on {T}witter",
    author = "Tonneau, Manuel  and
      Adjodah, Dhaval  and
      Palotti, Joao  and
      Grinberg, Nir  and
      Fraiberger, Samuel",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.453",
    doi = "10.18653/v1/2022.acl-long.453",
    pages = "6564--6587",
}