• Corpus ID: 251979496

The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach

@inproceedings{Langen2022TheIO,
  title={The Impact of the \#MeToo Movement on Language at Court -- A text-based causal inference approach},
  author={Henrika Langen},
  year={2022}
}

• Henrika Langen
• Published 1 September 2022
• Computer Science

This study assesses the effect of the #MeToo movement on different quantifiers of the 2015-2020 judicial opinions in sexual violence related cases from 51 U.S. courts. The judicial opinions are vectorized into bag-of-words and tf-idf vectors in order to study their development over time. Further, different indicators quantify to what extent the judges use a language that implicitly shifts some blame from the victim(s) to the perpetrator(s). These indicators measure how the grammatical structure, the sentiment and the context of sentences mentioning the victim(s) and/or perpetrator(s) change over time. The causal effect of the #MeToo movement is estimated by means of Difference-in-Differences comparing the development of the language in opinions on sexual violence and other interpersonal crime related cases as well as a Panel Event Study approach. The results do not clearly identify a #MeToo-movement-induced change in the language in court but suggest that the movement may have accelerated the evolution of court language slightly, causing the effect to materialize with a significant time lag. Additionally, the study considers potential effect heterogeneity with respect to the judge’s gender and his/her political affiliation. The study combines causal inference with text quantification methods that are commonly used for classification as well as with indicators from the fields of sentiment analysis, word embedding models and grammatical tagging.