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

III：小：协作研究：使用大规模图像数据进行在线社交媒体分析

• 批准号: 1115313
• 负责人: Eric Xing
• 金额: $ 20.42万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1115313&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Using

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.

由于个人电脑、移动的设备以及本地和全球互联网连接的技术进步，理解和分析我们世界的连接方式是当今世界的一个关键但新的挑战。 社交媒体分析、推理和理解领域的大多数当前方法都基于文本数据。然而，图像数据在社交媒体数据中所占的比例越来越大。因此，迫切需要能够有效地使用图像数据来提取重要信息以推断人、社区和整个社会的模式和活动的工具。该项目结合了计算机视觉、机器学习和社交网络的进步，以新颖的方式理解和分析大规模社交媒体数据。 该提案以新颖的方式将计算机视觉和机器学习研究结合在一起，以开发分析大规模社交媒体数据的新方法。它追求4个相互关联的目标：（i）通过众包、分类归纳和非参数学习方法，为网络图像世界建立一个大规模的视觉概念本体和结构;（ii）通过分析大规模和具有连通性的社交媒体背景下的图像内容，理解社交网络中的活动;（iii）从社交网络中的图像内容和个人活动推断社交网络和社区的结构;（iv）发现和分析动态社交媒体趋势。 这项研究的预期产品包括用于分析和建模社会生成内容的新工具，特别强调图像数据。由此产生的方法提供了潜在的有用的见解，用户，社区和社会的特点，在广泛的应用。 该项目为研究生和本科生提供强化的研究型高级培训机会，并涉及在斯坦福大学和卡内基梅隆大学就相关主题开发新课程。