Collaborative Research: SAI-R: Integrative Cyberinfrastructure for Enhancing and Accelerating Online Abuse Research

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership.

To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.

Online abuse is a pressing and growing societal challenge. Online hate and harassment, cyberbullying, and extremism threaten the safety and psychological well-being of targeted groups. Understanding the problem and developing ways to address it is the active focus of many fields of research in the social and behavioral sciences and in computer science.

Machine learning and the use of artificial intelligence (AI) offers great potential to support research in this area. Still, researchers face fundamental challenges in leveraging emerging machine learning techniques for innovative studies and scientific discoveries in online abuse. This SAI research project strengthens and transforms the current disperse machine learning software infrastructure.

It develops a scalable, customizable, extendable, and user-friendly Integrative Cyberinfrastructure for Online Abuse Research (ICOAR). The new infrastructure advances the research capability for scholars in different fields of science to leverage advanced machine learning methods for online abuse research. The ICOAR software infrastructure can be utilized by a large and growing number of researchers on online abuse detection and is a stimulus to research and innovation in AI for social good.

This project enables easy access to state-of-the-art machine learning techniques and datasets for rapid online abuse analysis. It supports and advances future investigations of new concepts and phenomena, assessments of prevalence, measures of causal effects, predictions, and evaluation of online abuse detection algorithms. ICOAR offers a modular and user-centered approach, ensuring future enhancements and long-term sustainability.

The open software infrastructure consists of three major layers: a data layer, a capability layer, and an application layer. The data layer includes tools for automatic data collection and preparation of online social media data from different sources, and access to public benchmark datasets. The capability layer is composed of modularized machine learning-based capabilities and algorithms for the study of online abuse.

The application layer allows researchers to easily develop different applications based on their research priorities. The ICOAR resources are open-source and provide an easy-to-use learning platform for curriculum development and workforce training. This award is supported by the Directorate for Social, Behavioral, and Economic (SBE) Sciences.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.