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

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也在迅速增长。对于不同学科的研究人员和实践者来说，这是一个巨大的“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，它们都是大规模的。社交媒体数据具有噪声大、不完整、多来源、多模态、多属性网络等特点。此外，这些数据不是独立和同分布的（i.i.d）。这些独特的属性为挖掘社交媒体数据带来了新的挑战。本项目研究了一种新的方法来特征选择在关联数据，特别是社交媒体数据。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。由于社交媒体数据来自多个有噪声的、部分的或冗余的来源，因此所提出的特征选择方法旨在选择相关的来源，并将它们一起用于指导多模态、多属性社交媒体中的链接特征选择。该项目是特征选择、社交媒体分析和数据挖掘的融合。该项目提供了一个机会，让那些擅长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和研究生课程以及学生研究项目中；为来自代表性不足群体的学生提供更多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，利用社交媒体应对危机和救灾，以及研究社会和政治运动。该项目的成果（包括出版物、软件等）将通过该项目的网址：http://www.public.asu.edu/~huanliu/projects/NSF12提供

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