We are the Soda (Social Data and AI) Lab at the School of Computing and Information Systems, Singapore Management University. We study social phenomena through large-scale data and computational tools, aiming to tackle big societal problems.
We focus particularly on human behavior on online platforms—the measurement, understanding, design, and assessment of implications. We use mobile devices any time to access the internet, read the news, watch videos, search for nearby restaurants, chat with friends, and leave posts on social networking sites. Those electronic footprints enable us to understand individual or collective human behavior: what people like or hate, how people feel about various topics, and how people behave and engage. Thus, it has become crucial to understand human behavior on these online platforms.
We develop new computational methods and tools for understanding, predicting, and changing human behavior on online platforms. One of the challenges posed by online data is the diversity and complexity of the datasets. We explore various types of large-scale data, investigate and compare existing tools to overcome its limitations and use them in the right way, and develop new measurements, machine learning models, and linguistic methods to understand human behaviors online and, furthermore, solve real-world problems.
However, our goal does not only solve real-world problems but those in online spaces. We are also interested in understanding obstacles to trusted public space online, developing methodologies to make them transparent, building frameworks to monitor them at large-scale in real-time, and transforming the public space online more credible.
We are located at Singapore Management University, School of Information Systems. Our university is in the heart of downtown Singapore.
We are looking for passionate new PhD students, Postdocs, and Master students to join the team (more info) !
Our work 'Estimating Homophily in Social Networks Using Dyadic Predictions ' is published in Sociological Science.22 July 2021
Our work 'FrameAxis: characterizing microframe bias and intensity with word embedding' is published in PeerJ Computer Science.1 July 2021
Haewoon was awarded the D.S. Lee Foundation Fellowship for a period of one year.21 June 2021
Jisun served as a PhD Symposium Chair at WebSci.10 June 2021
Jisun received the best reviewer award at ICWSM.8 June 2021
Haewoon received the best reviewer award at The Web.19 Feb 2021
Our work 'How to be successful in social media? A causal analysis of 8 media's news sharing practices on Twitter' is selected as an oral presentation in Computation+Journalism Symposium 2021.1 Jan 2021
Jisun serves as an associate editor of EPJ Data Science starting from 2021.