About Us
In the twenty-first century, researchers live in a complex information environment with fast-paced technological advances and constantly growing computational power. We have access to vast amounts of digital information that traditional social scientific methods were not designed to handle. Against this backdrop, the social sciences increasingly rely on computational methods to explore socio-technical systems. This has led to the emergence of computational social science (CSS), an interdisciplinary field combining computer science tools and techniques with social science theories and methods to understand human behavior. CSS allows us to study complex systems and dynamic processes at the individual, community, and societal levels.
The diverse members of the CSS Lab have strengths in computational social science, data science, network analysis, natural language processing, machine learning, algorithmic audits, and others. Combined with domain expertise in communication, media studies, library and information science, this allows us to conduct cutting-edge research exploring the role of digital platforms in social processes.
We are closely connected with other initiatives at Rutgers, including the NetSCI Lab, the Behavioral Informatics Lab, and the Social Media & Society Cluster.
The lab has been funded by the School of Communication & Information‘s Grants for Team-based Faculty Research and Scholarly Activity.
Our Mission
The lab was created with view to serving the Rutgers community in the following ways:
- Facilitating collaborative research
- Supporting the development of grant proposal in the area of Computational Social Science
- Creating a shared identity for Computational Social Science scholars at Rutgers
- Increasing the visibility of Rutgers University in the area of Computational Social Science
- Recruitment of graduate students interested in Computational Social Science
- Mentoring, training, and collaborating with students interested in Computational Social Science
- Offering hands-on Computational Social Science training to faculty working in this area
- Sharing expertise with other scholars less versed in computational methods
- Pooling and coordinating resources (e.g. data, digital tools, etc.)