III: Small: Transforming Feature Selection to Harness the Power of Social Media

III：小：转变特征选择以利用社交媒体的力量

• 批准号: 1217466
• 负责人: Huan Liu
• 金额: $ 41.04万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217466&HistoricalAwards=false
• 关键词: III; Small; Transforming; Feature; Selection

The growth of social media data in size and variety accelerates rapidly as more people use social media such as Facebook, Twitter, LinkedIn, among others. It is a massive "treasure trove" interesting to researchers and practitioners of different disciplines, and a great source for data mining. However, attribute-value data in classic data mining differs from social media data besides both are large-scale. Social media data are noisy, incomplete, comprised of multiple sources, and form multi-modal and and multi-attributed networks. Furthermore, such data are not independent and identically distributed (i.i.d.). These unique properties present new challenges for mining social media data. This project investigates a novel approaches to feature selection in linked data in general and social media data in particular. Specifically, it seeks to exploit link information in supervised as well as unsupervised feature selection for social media data. Because social media data are drawn from multiple noisy, partial, or redundant sources, the proposed approach to feature selection seeks to select relevant sources and use them together to guide linked feature selection in multi-modal, multi-attributed social media.The project lies at the confluence of feature selection, social media analysis, and data mining. The project offers an opportunity to engage students who are adept users of social media in developing computational tools that can harness the power of social media. Some broader impacts of this research include integration of social media analytics into undergraduate and graduate courses as well as student research projects; enhanced research-based training opportunities for students from under-represented groups; and powerful social media analytics tools for understanding collective behavior in social media, employing social media for crisis response and disaster relief, and studying social and political movements. The results of the project (including publications, software, etc.) will be made available through the project web site: http://www.public.asu.edu/~huanliu/projects/NSF12

随着越来越多的人使用 Facebook、Twitter、LinkedIn 等社交媒体，社交媒体数据的规模和种类迅速增长。它是不同学科的研究人员和从业者感兴趣的巨大“宝库”，也是数据挖掘的重要来源。然而，经典数据挖掘中的属性值数据与社交媒体数据不同，而且两者都是大规模的。社交媒体数据充满噪音、不完整、由多个来源组成，并形成多模式和多属性网络。此外，此类数据不是独立同分布 (i.i.d.)。这些独特的属性给挖掘社交媒体数据带来了新的挑战。该项目研究了一种在一般链接数据（特别是社交媒体数据）中进行特征选择的新方法。具体来说，它试图利用社交媒体数据的监督和无监督特征选择中的链接信息。由于社交媒体数据是从多个噪声、部分或冗余源中提取的，因此所提出的特征选择方法旨在选择相关源并将它们一起使用来指导多模式、多属性社交媒体中的链接特征选择。该项目在于特征选择、社交媒体分析和数据挖掘的融合。 该项目提供了一个机会，让熟练使用社交媒体的学生参与开发可以利用社交媒体力量的计算工具。这项研究的一些更广泛的影响包括将社交媒体分析整合到本科生和研究生课程以及学生研究项目中；为代表性不足群体的学生提供更多基于研究的培训机会；强大的社交媒体分析工具，用于了解社交媒体中的集体行为，利用社交媒体进行危机应对和救灾，以及研究社会和政治运动。项目成果（包括出版物、软件等）将通过项目网站提供：http://www.public.asu.edu/~huanliu/projects/NSF12