đź‘Ź Hello!
I'm Jingwei Gao, a first-year PhD student in the Department of Media and Comm at City University of Hong Kong, under the supervision of Prof. ZHU Jian Hua Jonathan.
My current interest focuses on understanding how multimodal mental health information is disseminated on social media—by whom and what types of content—and its impact on various groups, such as individuals with mental health issues and the general public.
Before starting my PhD, I earned a Master's degree in Communication from Peking University HSBC Business School in 2021, where I was advised by Dr. Weiming Ye. After that, I worked as a Product Designer at Tencent Financial Technology for one year.
Check my Curriculum Vitae for more info.
🌟Featured
Method | Structural Equation Models
Posted on:December 27, 2023SEM is an umbrella term referring to a framework of linear models designed to explore relationships among both observable and unobservable variables.
Method | Linear Mixed Models
Posted on:December 12, 2023A Linear Mixed Model is a statistical model that is particularly useful for analyzing data that have both fixed and random effects, which are pretty common in fields like psychology.
Method | Regression Analysis
Posted on:April 30, 2023A comprehensive guide to regression analysis covering both continuous and discrete data, tailored for social science students involved in quantitative research.
Method | Network Analysis
Posted on:April 16, 2023An introduction to social network analysis, spanning analysis techniques from individual nodes to group dynamics, incorporating descriptive and inferential methodologies.
📆Recent Posts
Statistics | Poisson Model
Posted on:January 16, 2025The Poisson distribution is often used to count the number of “successes” in scenarios involving a large number of trials, each with a small probability of success.
Statistics | The Bootstrap
Posted on:January 10, 2025Bootstrap is a powerful statistical tool used to quantify the uncertainty associated with a given statistic or estimator.
Network | Node Embeddings
Posted on:December 15, 2024The goal is to represent nodes in an embedding space where the distances between them correspond to their similarities in the original network.
Method | A Quick Introduction to Causal Inference
Posted on:December 1, 2024This quick introduction covers basic conceptual frameworks in causal inference and three practical methods.
Method | A Crash Course For Bayesian Inference
Posted on:November 1, 2024Bayesian inference is essentially about using observed data to update prior knowledge about the population, resulting in what is called the posterior.