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Advances in AI for web integrity, equity, and well-being.

¹Georgia Institute of Technology, Atlanta, GA, United States.

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Summary

This research uses AI and machine learning to detect and stop online harms like hate speech and misinformation. It aims to build a safer internet through proactive, data-driven solutions.

Keywords:

applied machine learning artificial intelligence bad actors data mining misinformation social networks

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Area of Science:

Computer Science
Artificial Intelligence
Data Mining
Machine Learning
Social Science

Background:

Online platforms face increasing threats from malicious actors and harmful content.
Current methods for combating online harms are often slow and reactive.
There is a need for proactive, socially-aware, and interdisciplinary solutions.

Purpose of the Study:

To develop advanced data mining, AI, and machine learning methods to combat online threats.
To create a trustworthy online ecosystem promoting user health, equity, and integrity.
To shift from reactive to agile, proactive, and whole-of-society approaches against cyberharms.

Main Methods:

Novel graph, content (NLP, multimodality), and adversarial machine learning techniques.
Leveraging terabytes of data for detection, prediction, and mitigation of online threats.
Interdisciplinary approach combining computer science with social science theories.

Main Results:

Four research thrusts: detection, robust prediction, impact attribution, and mitigation.
Development of holistic solutions to combat cyberharms across platforms and modalities.
Successful deployment and influence of developed models in real-world applications (e.g., Flipkart, Twitter, Wikipedia).

Conclusions:

The research provides a comprehensive framework for combating online harms.
The developed socio-technical solutions contribute to a safer and more trustworthy online environment.
Future work focuses on advancing socially-aware methods for enhanced online integrity.