Analysing Impact of COVID-19 on US Child Welfare
NLP & computational social science project analysing child welfare casenotes to study COVID-19 impacts on US child welfare systems.
| 2022 – 2023 | Topic Modelling · Sentiment Analysis · NLP · Computational Social Science |
Course project for Computational Social Science course by Dr. Ashton Anderson (Computer Science, University of Toronto), in collaboration with Erina Moon.
Analysed over 12,566 casenotes from a US Child Welfare (CW) Agency to investigate how COVID-19 affected child welfare operations and caseworker practices.
Methods
- Conducted topic modelling, sentiment analysis, and open coding on CW Agency casenotes
- Applied NLP techniques to uncover patterns in narrative casework data during the pandemic
Outcome: Poster Examining the Impact of COVID-19 on US Child Welfare Systems presented at Upper Bound Conference 2023, Alberta Machine Intelligence Institute (AMII), supported by an AMII Talent Bursary.
Links: Documentation · Poster