Social Computing Lab, led by Jana Diesner

Current Projects | Examples of Completed Projects

Examples of completed grants and projects:

Impact Assessment
Impact of works of art on society
Computational impact assessment of issue focused media and information products

Funder: FORD Foundation

This project proposed a solution for measuring impact of social justice documentaries in a theoretically grounded, systematic, empirical, scalable and rigorous fashion using computational approaches and to gain novel substantive knowledge providing actionable insights for film makers and funders...[Learn More]
Other impact assessment projects
Crisis Informatics
Network analysis and information extraction for Humanitarian Assistance and Disaster Relief (HADR)
Reliable Extraction of Emergency Response Networks from Text Data and Bench-marking with National Emergency Response Guidelines, 2019-2020
This project employs techniques from natural language processing and social network analysis to identify and evaluate multi-modal networks involved in Humanitarian Assistance and Disaster Relief (HADR) efforts. This project contains the following 3 research components:
  • Relation extraction: evaluation of methods for extracting relational data from texts.
  • Link labeling: development of domain specific model and method for edge classification.
  • Network comparison: comparison of extracted networks to mandated interaction networks.
...[Learn More]
Machine learning and natural language processing for Humanitarian Assistance and Disaster Relief (HADR)
Review and Assessment of the Usage of Computational Methods for Humanitarian Assistance and Disaster Relief (HADR) Efforts, and Scalable Measurement of Emergency Response from Text Data, 2017-2018
The Humanitarian Assistance and Disaster Relief (HADR) project has endeavored to use natural language processing (NLP) to increase situational awareness of humanitarian relief operations. Our methods-oriented approach augments existing efforts by comparing different sources of text-based communication to an established ground truth...[Learn More]
Social Computing for Intelligence Analysis
Internet of battlefield things
Alliance for IoBT Research on Evolving Intelligent Goal-driven Networks (IoBT REIGN), 2017-2022
The IoBT REIGN is a multi-institutional initiative funded by the Army Research Lab (under W911NF-17-2-0196) to enable new predictive battlefield analytics and services. Our team focuses on the following research component:
  • Morality, stance, and images: measuring individuals’ perception from user-generated texts when they are exposed to various types of information with embedded stimuli.
Biases in Data and Technology, and Data Quality and Provenance; Scientometrics
KISTI (Korea Institute of Science and Technology Information) Projects

Project Description:
Dr. Jana Diesner has led a three-phased project spanning over three years (2014~2016) in order to measure how data quality (name ambiguity) can affect our micro- and macro-level knowledge findings from bibliometric data including that generated and maintained by KISTI (Phase 1), based on quality-controlled KISTI data investigate the structure and evolution of collaboration patterns among almost 700,000 Korean scholars over the 65 years (Phase 2), and identify what mechanisms govern collaborator selection and influence among scholars drawing on social theories of human interaction (Phase 3).
Major findings include that (1) data quality can severely distort our understanding of bibliometric data, contrary to wide assumptions among informetrics scholars that such distortion is ignorable, (2) scholarly collaboration in Korea have grown exponentially but shown trends toward a highly centralized, hierarchical structure depending heavily on a small number of top scholars, and (3) traditional network-structure-based analysis do not explain or predict much formation of collaboration among scholars, leading Dr. Diesner to propose a new framework for detecting the impact of influence in scientific collaboration.

Funder: KISTI (Korea Institute of Science and Technology Information)

Data Compliance and Governance
Academia-Industry Big Data Collaboration for Early Career Researchers program: Developing organizational expertise and resources for the responsible conduct of research with human generated and publicly available data, 2016

Project Description:
Researchers frequently collect, use, and analyze publicly available data from social networking platforms, online production communities, and customer review websites, among other sources, as part of their research activities. Technically, it can be feasible and straightforward to access and obtain public data, while considering the ethics, norms, and regulations applicable to these data requires additional awareness, knowledge and skills. This is partially due to the fact that multiple types of rules may apply. We provide an overview on these types of regulations, and also clarify on common misassumptions between free as in “free speech” (i.e. freedom from restriction, “libre”) versus free as in free beer (i.e. freedom from cost, “gratis”). We outline several approaches to responsibly conducting research with public available data, and solutions to enhancing the expertise of researchers on implement data regulations, especially for the case of human centered data science.

Funder: Midwest Big Data Hub & Computing Community Consortium

Information Extraction and Evaluation

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