@article{THESIS,
      recid = {3811},
      author = {Li, Qiuyu},
      title = {Social Media, Misogyny, and Labor Market Outcomes},
      publisher = {University of Chicago},
      school = {M.A.},
      address = {2022-06},
      number = {THESIS},
      abstract = {The practice of employing social media data as the monitor  of public discourses and opinions has become increasingly  popular with the development of big data techniques and  machine learning tools. In this project, I intend to show  that social media data can be used to refine the  measurement of people’s gender-related attitudes, as a  supplement to the traditional survey data. And more  importantly, such finer-grinded and cheap-to-collect data  may imply prevailing discriminations in local labor  markets. To achieve this purpose, I trained a BERT  classifier on a publicly available annotated misogyny  dataset, and used it to select terms that are highly likely  to be included in misogynistic tweets. In addition, I  calculated weights for each terms as a measurement of how  “efficient” they were in identifying misogynistic tweets. I  then used the terms as well as their corresponding weights  to construct a county-level Twitter Misogyny Index, and  explored its implications on gender gaps in earnings and  labor market participations. The results supported Becker  (1957)’s taste-based discrimination model, and that each  one standard deviation increase in the Twitter Misogyny  Index is associated with a 0.02 increase in men-to-women  median earnings ratio in case of all 16-year-old-and-above  workers.},
      url = {http://knowledge.uchicago.edu/record/3811},
      doi = {https://doi.org/10.6082/uchicago.3811},
}