Man Pui Sally Chan

Profile picture for Man Pui Sally Chan

Contact Information

Room 738/781
603 E Daniel St
Champaign
IL 61820
Research Assistant Professor

Biography

 

Man-pui Sally Chan has a background in information systems and received her Ph.D. in Psychology from the University of Hong Kong in 2014. She awarded a post-doctoral fellowship in the Cambridge Prosociality and Well-Being Lab in the UK. Sally has joined the Social Action Lab in the Psychology Department at the University of Illinois at Urbana-Champaign since late 2015. Her research concerns the underlying psychological mechanisms behind health-related behaviors and decisions in health care contexts. She approaches these problems with a unique combination of psychology theory and data science by incorporating the analysis of surveillance data, secondary datasets, surveys, and social media data (big data) to model patterns of infections and changes of behaviors. Her research today has sought to (a) develop social-media based prediction models of epidemiology and health product use, (b) identify the impact of social media on health behaviors, and (c) create novel, scalable methods of recommendation and intervention for health promotion.

 

Research Interests

I am interested in using a combination of machine learning techniques and inferential statistical analyses to explore how online social communications can be utilized to understand individuals’ dispositional characteristics, attitudes/beliefs, as well as private and public health behaviors. My current research focuses on using social media data to model infectious diseases projections, examine the dynamics of the transmission and treatment of infectious diseases (e.g., HIV/STIs), and develop machine learning models to evaluate the actionability of text messages in HIV prevention and care.

One of my ongoing projects is about developing a new method that collects annotations for images posted on social media regarding visual rhetoric, perceived effectiveness, and acceptability. Surprisingly, this vital component of image analytics has not been explored using social media data but is essential given the increasingly frequent use of images on social media and the Internet. I plan to scale up this project to study different health-related prevention behaviors. 

Another line of research is about debunking health-related misconceptions/misbeliefs. Misconceptions often lead to poor decisions about consequential matters and are persistent and difficult to correct. The effects are of interest to many areas including, but not limited to, psychology, public health, and public policy. I have carried out a series of experiments and analyses, together with a team of psychologists and public policy scholars, to move beyond individual psychology experiments to estimate the robustness of the phenomenon and to provide empirical evidence to evaluate methods to correct false information.