@article{THESIS,
      recid = {4162},
      author = {Hartnett, Sabina},
      title = {Analyzing Gender Representation in News Media using  Automated Methods},
      publisher = {University of Chicago},
      school = {M.A.},
      address = {2022-08-19},
      number = {THESIS},
      abstract = {Through a multitude of platforms and sources, news media  permeates online daily interactions. This reach affords  news media significant social influence. Analyzing news  articles at scale can reveal latent trends in news media,  which ultimately have the potential to be norm-setting. In  this study, we implement computational tools to reveal  large scale trends in news reporting. Specifically, we  integrate NER parsing, record linkage (in the form of  gender prediction), topic modeling and word embeddings to  reveal trends both in the corpus overall, as well as  specific to gendered contexts. Named Entity Recognition and  record linkage to isolate contexts in which an individual  is reported on (and predict the individual’s gender in  order to make larger claims about gender representation in  news media). These contexts are then used to train the word  embeddings: illuminating differences in the semantic  contexts and roles for women/men in news media contexts.  This study contributes to an emerging field at the  intersection between machine learning and quantitative  social science by implementing advanced model architectures  to answer questions based in cultural and media studies.},
      url = {http://knowledge.uchicago.edu/record/4162},
      doi = {https://doi.org/10.6082/uchicago.4162},
}