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Gender Classification Models and Feature Impact for Social Media Author Profiling

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Book cover Evaluation of Novel Approaches to Software Engineering (ENASE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1556))

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Abstract

Automatic profiling models infer demographic characteristics of social network users from their generated content or interactions. Due to its use in business (targeted advertising, market studies...), automatic user profiling from social networks has become a popular task. Users’ demographic data is also crucial information for more socially concerning tasks, such as automatic early detection of mental disorders. For this type of users’ analysis task, it has been demonstrated that the way users employ language is an essential indicator that contributes to the effectiveness of the models. For this reason, we also believe that considering the usage of the language from both psycho-linguistic and semantic characteristics it is useful for detecting variables such as gender, age, and user’s origin. A proper selection of features will be critical for the performance of retrieval, classification, and decision-making software systems, a proper selection of features will be critical. In this work, we shall discuss gender classification as a part of the automated profiling task. We present an experimental analysis of the performance of existing gender classification models for automated profiling based on external corpus and baselines. We also investigate the role of linguistic characteristics in the model’s classification accuracy and their impact on each gender. Following that analysis, we have developed a feature set for gender classification models in social networks that outperforms existing benchmarks in terms of accuracy.

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Notes

  1. 1.

    https://pan.webis.de/.

  2. 2.

    https://pan.webis.de/shared-tasks.html.

  3. 3.

    https://pan.webis.de/data.html.

  4. 4.

    https://pan.webis.de/data.html.

  5. 5.

    https://pan.webis.de/clef20/pan20-web/celebrity-profiling.html.

  6. 6.

    https://scikit-learn.org/stable/.

  7. 7.

    https://www.nltk.org/.

  8. 8.

    https://spacy.io/.

  9. 9.

    https://pyphen.org/.

  10. 10.

    https://github.com/carpedm20/emoji/.

  11. 11.

    https://pan.webis.de/data.html.

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Acknowledgements

This work was supported by projects RTI2018-093336-B-C21, RTI2018-09333 6-B-C22 (Ministerio de Ciencia e Innvovación & ERDF) and the financial support supplied by the Consellería de Educación, Universidade e Formación Profesional (accreditation 2019-2022 ED431G/01, ED431B 2019/03) and the European Regional Development Fund, which acknowledges the CITIC Research Center in ICT of the University of A Coruña as a Research Center of the Galician University System.

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Authors and Affiliations

  1. IRLab, CITIC Research Centre, Universidade de Coruña, A Coruña, Spain

    Paloma Piot-Perez-Abadin, Patricia Martin-Rodilla & Javier Parapar

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  1. Paloma Piot-Perez-Abadin
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  2. Patricia Martin-Rodilla
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  3. Javier Parapar
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Correspondence to Paloma Piot-Perez-Abadin .

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Editors and Affiliations

  1. Hamad bin Khalifa University, Doha, Qatar

    Raian Ali

  2. TU Wien, Vienna, Austria

    Hermann Kaindl

  3. Wrocław University of Economics, Wrocław, Poland

    Leszek A. Maciaszek

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Piot-Perez-Abadin, P., Martin-Rodilla, P., Parapar, J. (2022). Gender Classification Models and Feature Impact for Social Media Author Profiling. In: Ali, R., Kaindl, H., Maciaszek, L.A. (eds) Evaluation of Novel Approaches to Software Engineering. ENASE 2021. Communications in Computer and Information Science, vol 1556. Springer, Cham. https://doi.org/10.1007/978-3-030-96648-5_12

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  • DOI: https://doi.org/10.1007/978-3-030-96648-5_12

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