My experience lies in developing online behavioral experiments by leveraging programming techniques such as R, python, and javascript. I also have a background in data science, particularly in the realm of text mining. By employing cutting-edge text mining methodologies, such as advanced topic modeling, I can uncover latent structures within texts. This unique combination of skills enables me to construct and analyze online behavioral experiments effectively while also delving into the depths of text data to extract valuable insights I am currently serving as a postdoc researcher in the ASCoR. Collaborating with Dr. Eline Smit and Dr. Saar Mollen, my research delves into the intricate relationship between individuals' social media usage and their exposure to food-related content. To do this, we have employed a multidisciplinary approach, including a novel data collection method known as data donation. Our objective is to unravel the intricate connections between social media engagement and individuals' dietary content exposure, ultimately shedding light on the underlying mechanisms at play.

Expertise and research fields

• Computational Social Science
• Topic Modeling
• Public Health
• Online Behavioral Experiment

Other links

ACHC research team: https://www.healthcommunication.nl/eng/2022/12/28/dr-kaiyang-qin/ 

Research methods

• Online Behavioral Experiment
• Survey
• Lab Experiment

Current reseach projects

Mapping digital food environment on social media

Research grants & honours

• Kurt Lewin Institute seed grant 2022 - 06

MA

• Research method tailered to the thesis
• Digital Analysis (Guest lecturer)