III: Small: Collaborative Research: Using Large-Scale Image Data for Online Social Media Analysis

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Understanding and analyzing the way our world is connected is a critical but new challenge in today's world, thanks to the technological advances of personal computers, mobile devices, as well as local and global Internet connections. Most current methods in the area of social media analysis, inference and understanding are based on textual data. However, the image data makes an increasingly large proportion of data in social media. Hence, there is an urgent need for tools that can effectively use image data to extract important information to infer patterns and activities of people, communities and society at large. This project combines advances in computer vision, machine learning, and social networks in novel ways for understanding and analyzing large-scale social media data. The proposal brings together computer vision and machine learning research in novel ways to develop new methods for analyzing large-scale social media data. It pursues 4 inter-related aims: (i) Establishing a large-scale visual concept ontology and structures for the web-image world via crowdsourcing, taxonomy induction, and nonparametric learning methods; (ii) Understanding activity in social networks by analyzing image contents in the context of social media in large-scale and with connectivity; (iii) Inferring the structure of social networks and communities from image contents and activity of individuals in social networks; (iv) Discovering and analyzing dynamic social media trends. Anticipated products of this research include new tools for analysis and modeling of socially generated content, with special emphasis on image data. The resulting methods provide potentially useful insights that characterize users, communities and societies, in a broad range of applications. The project offers enhanced research-based advanced training opportunities for graduate as well as undergraduate students and involves development of new courses on related topics at both Stanford University and Carnegie Mellon University.