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。它是一个巨大的“宝库”，对不同学科的研究人员和实践者来说都很有趣，也是数据挖掘的重要来源。然而，传统数据挖掘中的属性值数据不同于社交媒体数据，而且两者都是大规模的。社交媒体数据是嘈杂的，不完整的，由多个来源组成，并形成多模态和多属性网络。此外，这些数据不是独立同分布（i.i.d.）的。这些独特的属性为挖掘社交媒体数据提出了新的挑战。该项目研究了一种新的方法来选择链接数据中的特征，特别是社交媒体数据。具体来说，它旨在利用链接信息进行社交媒体数据的监督和无监督特征选择。由于社交媒体数据来自多个噪声、部分或冗余源，因此所提出的特征选择方法旨在选择相关源，并将它们一起用于指导多模态、多属性社交媒体中的关联特征选择。该项目融合了特征选择、社交媒体分析和数据挖掘。 该项目提供了一个机会，让那些擅长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和研究生课程以及学生研究项目中;为代表性不足的群体的学生提供增强的基于研究的培训机会;以及强大的社交媒体分析工具，用于理解社交媒体中的集体行为，将社交媒体用于危机应对和救灾，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