Advika Jhingran
Pronouns: she/her
Research Mentor(s): Elyse Thulin
Research Mentor School/College/Department: Addiction Center, Psychiatry & Michigan Data Science (MIDAS) / Medicine
Program: UROP
Session: Session 2 (10:00am – 10:50am)
Authors: Elyse Thulin
Abstract
This project aims to use natural language processing techniques (NLP) on reddit posts from the r/leaves subreddit (a popular subreddit on cannabis cessation) to better understand patient recovery from cannabis. Natural language processing is an integrative subfield of linguistics, computer science, and artificial intelligence that looks at how code can prepare and analyze vast amounts of natural language data. Reddit is a social media platform on which users create posts to a specific community (subreddit) to look for information or social support. As connection is essential to recovery, these online communities such as Reddit may provide support to these individuals struggling with addiction. In this study, we investigate the differences between the posts with high engagement versus those with low engagement. To do this, a variety of NLP techniques will be used. The data (r/leaves subreddit posts) was first collected through Pushshift. Then, it was tokenized and normalized (with appropriate punctuation, stopword, spacing removal) with the NLTK package. We are currently trying to see a difference in either the topic, length, sentiment, structure, or type of post between the two classifications. So far, posts with higher engagement tend to be more positive while those with lower engagement have a more neutral sentiment. From this, a classifier can be constructed that determines whether a post will be engaged with highly or not. A deeper understanding on the characteristics of high and low engagement posts could help inform future intervention and public health efforts to increase access for social support for those users wanting to change their behavior in regards to cannabis.
