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Predicting the demographics of Twitter users with programmatic weak supervision

  • Jonathan Tonglet [1] ; Astrid Jehoul [2] ; Manon Reusens [1] ; Michael Reusens [1] ; Bart Baesens [1]
    1. [1] KU Leuven

      KU Leuven

      Arrondissement Leuven, Bélgica

    2. [2] Datashift, Oude Brusselsestraat, 14, 2800, Mechelen, Belgium
  • Localización: Top, ISSN-e 1863-8279, ISSN 1134-5764, Vol. 32, Nº. Extra 3, 2024 (Ejemplar dedicado a: Mathematical Optimization and Machine Learning), págs. 354-390
  • Idioma: inglés
  • DOI: 10.1007/s11750-024-00666-y
  • Enlaces
  • Resumen

    • Predicting the demographics of Twitter users has become a problem with a large interest in computational social sciences. However, the limited amount of public datasets with ground truth labels and the tremendous costs of hand-labeling make this task particularly challenging. Recently, programmatic weak supervision has emerged as a new framework to train classifers on noisy data with minimal human labeling efort. In this paper, demographic prediction is framed for the frst time as a programmatic weak supervision problem. A new three-step methodology for gender, age category, and location prediction is provided, which outperforms traditional programmatic weak supervision and is competitive with the state-of-the-art deep learning model. The study is performed in Flanders, a small Dutch-speaking European region, characterized by a limited number of user profles and tweets. An evaluation conducted on an independent hand-labeled test set shows that the proposed methodology can be generalized to unseen users within the geographic area of interest.

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