“Is this Herpes or Syphilis?”: Latent Dirichlet Allocation Analysis of Sexually Transmitted Disease-Related Reddit Posts During the COVID-19 Pandemic

作者：Amy K. Johnson, Runa Bhaumik, Debarghya Nandi, Abhishikta Roy, Supriya D. Mehta · 发表于：medRxiv · 年份：2022 · DOI：10.1101/2022.02.13.22270890 · 被引用次数：1 · 研究领域：Misinformation and Its Impacts、Hate Speech and Cyberbullying Detection、Topic Modeling

Abstract Background Sexually Transmitted Diseases (STDs) are common and costly, impacting approximately one in five people annually. Reddit, the sixth most used internet site in the world, is a user-generated social media discussion platform that may be useful in monitoring discussion about STD symptoms and exposure. Objective This study sought to define and identify patterns and insights into STD related discussions on Reddit over the course of the COVID-19 pandemic. Methods We extracted posts from Reddit from March 2019 through July 2021. We used a machine learning text mining method, Latent Dirichlet Allocation (LDA), to conduct a text analysis to identify the most common topics discussed in the Reddit posts. We then used word clouds, qualitative topic labelling, and spline regression to characterize the content and distribution of topics observed. Results Our extraction resulted in 24,311 total posts. LDA Coding showed that with 8 topics for each time period we achieved high coherence values (pre-COVID=0.41, pre-vaccine=0.42; post-vaccine=0.44). While most topic categories remained the same over time, the relative proportion of topics changed and new topics emerged. Spline regression revealed some key terms had variability in the percentage of posts that coincided with COVID-19 pre- and post-periods, while others were uniform across the study periods. Conclusions Our study’s use of Reddit is a novel way to gain insights into STD symptoms experienced, potential exposures, ...