Programming Language(s) used: python, SQL
Description: This project used logitudinal data from the General Social Survey (GSS) to examine how public perception of scientific instutions was altered by the COBID-19 pandemic (2018 to 2021). Topics explored include cultivation theory, mental health ratings, and the effect of one's occupation on opinions on govermental responsiblity for public health.
Programming Language(s) used: R studio
Description: This project was an experiment conducted to examine the effect of AI journalist source cues on audience trust and ratings of efficacy in mental health articles. A population of ~120 was surveyed via a blind qualtrics experiment. Age and familarty with AI were shown to be signficinat predictors of trust in AI journalists.
Programming Language(s) used: python, OpenAI API, R studio, structural topic modeling (STM) & Emotive Indicators Lexicon packages
Description: My thesis tested ChatGPT3.5's ability to produce effective climate policy appeals when prompted to target specific partisan identities. Python scripts were written to collect data from OpenAI's API, as well as clean and analyze that data. The structural topic modeling (STM) unsupervised machine learning model was used to derive message frames procuded by the large language model. Results indicate small but significant differences across most indicators examined: e.g., GPT uses more superoridnate langauge for democrats when creating climate policy messaging, more in-group source cue messaging for republicans. Signficant differences were also found in the type of frames used for each partisan group, though these didn't always match up with documented best practices in climate communicaiton literature.