Social media platforms like Twitter, Instagram, and Facebook are a hotbed of activity and therefore provide unprecedented opportunities for the gathering of big data. While there are certainly ethical and privacy considerations, the availability of these large quantities of data, in both imagery and text forms, allow for the use of artificial intelligence approaches for analytics. Natural language processing techniques can yield insights into sentiment analysis around important topics such as breaking news and disease diagnoses. Computer vision-based methods can harness the wide range of photos posted on social networks to inform disaster relief pipelines in crisis situations and analyze the change in urban areas over time. In any case, the unique quality of social media as vehicles for the widespread and easily facilitated dissemination of data means that there will continue to be exciting AI-driven innovations in the future. These innovations will help to save lives, optimize energy and create a more sustainable society, and enable solutions that tackle mental health issues. Of course, the challenges associated with this work include the privacy of the data posted on the platforms and legal issues regarding web scraping. Another challenge is deploying certain social media analytics technologies into the real world for use by everyday folks. Making sure that the technology is accessible and interpretable is key, especially when talking about typical "black box" models like convolutional neural networks (CNNs). Regardless, social media's applications in the field of AI are exciting, and we discuss current and future ramifications in this session.
PRO TALK (AI): Social Media Data for AI Applications: Unprecedented Opportunities and Ethical Considerations
Thomas Chen is a machine learning researcher from the New York Metro area in the USA who is passionate about machine learning, computer vision, and artificial intelligence. He is highly involved in applying ML and AI to real-world issues that face society (e.g. deep learning-based computer vision for damage assessment post-natural disaster). He has been an invited speaker at conferences like the IEEE Conference on Technologies for Sustainability, American Geophysical Union Fall Meeting, and the Energy Anthropology Network. Thomas has also spoken at event like NeurIPS and CVPR workshops, Applied Machine Learning Days, the Open Data Science Conference, and Machine Learning Week Europe. He is considered a leading expert in the area of machine learning-driven earth observation (EO) applications. Thomas is a member of the U.S. Technology Policy Committee (Association for Computing Machinery) and the Research Data Alliance.