Jingwen Zhang

Convergence of mobile and online technologies provides Jingwen Zhang with unprecedented opportunities to implement large-scale field experiments to study human behavior.
Jingwen Zhang

Jingwen Zhang

Zhang, an assistant professor of communication (PhD from the University of Pennsylvania, 2016), works with computer scientists and developers to design websites and mobile apps to study how different social network structures influence people’s behaviors and decision-making processes.

Constructing Online Networks for Positive Social Influence

While endogenously evolving online social networks like Facebook and Twitter have been studied extensively during the past decade, constructed online networks and their effects remain a relatively underdeveloped terrain. Zhang thinks constructed artificial online networks as a form of persuasive technology. Experiment subjects in Zhang’s research are embedded into different online network structures without their knowledge. As subjects interact with other subjects in the experiment, their interaction patterns are constrained by the different online networks, which ultimately generate different collective behavior patterns. 

People have very poor understanding of their extended social networks. However, these invisible social networks with the information, thoughts, and emotions flowing through them, play a significant role in influencing what one thinks and how one thinks. Using highly controlled experimental online networks provides Zhang with a unique approach to unpack such invisible influences and find the best network designs to promote positive behaviors.

In three field experiments, Zhang put individuals into constructed online networks and allow them to track their network neighbors’ physical activity behaviors. For a period of 3 months, Zhang tracked every subject’s online behavior and offline behavior. In one study, she gave Fitbit to every subject to monitor her activity levels. The findings clearly indicate that social influence realized through online networks significantly changed individuals’ physical activity behaviors. Most interestingly, the findings suggest that networks that involve people who are similar to each other with regard to activity levels were the most effective in encouraging everyone to exercise. 

Mobile Technologies

Although mobile apps have continued to dominate digital engagement and have demonstrated powerful functions for data collection, relatively few social scientists have leveraged apps as an experimental platform. Zhang performed a systematic review on mobile app-based field experiments and found that scholars have only started to employ apps in field experiments in the last 4 years. Almost all studies were from the health research domain and only 7 studies utilized smartphone sensors for collecting data. As a social scientist, Zhang thinks that given mobile apps’ powerful capabilities in reaching to and collecting data from people in their natural living environments, mobile apps need to be seriously considered more in advancing social experiments. 

For more see her personal site.