Profiling Dutch Authors on Twitter: Discovering Political Preference and Income Level

Authors

  • Reinder Gerard van Dalen University of Groningen, The Netherlands
  • Léon Redmar Melein University of Groningen, The Netherlands
  • Barbara Plank University of Groningen, The Netherlands

Abstract

Research in author profiling has primarily focused on English-speaking users and attributes like age, gender and occupation. We present first experiments on automatic profiling Dutch Twitter users for two less-studied attributes, namely their political preference and income level (low vs high). We create two novel corpora using distant supervision, evaluate the corpus creation approach, and train predictive models for each attribute. Our empirical evaluation shows that distant supervision is surprisingly reliable and political preference and income level of Dutch users can be predicted relatively accurately from the linguistic input. We also discuss which features are predictive for income and political preference, respectively.

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Published

2017-12-01

How to Cite

van Dalen, R. G., Melein, L. R., & Plank, B. (2017). Profiling Dutch Authors on Twitter: Discovering Political Preference and Income Level. Computational Linguistics in the Netherlands Journal, 7, 79–92. Retrieved from https://www.clinjournal.org/clinj/article/view/70

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Articles