NSF EAGER: Data-Driven Framework for Analyzing User Interactions in Social Media

NSF EAGER：用于分析社交媒体中用户交互的数据驱动框架

• 批准号: 1135389
• 负责人: Divyakant Agrawal
• 金额: $ 19.99万
• 依托单位: University of California-Santa Barbara
• 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: 2011-05-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1135389&HistoricalAwards=false
• 关键词: NSF; EAGER; Data; Driven; Framework

With hundreds of millions of users worldwide, social networks provide incredible opportunities for socialinteractions, entertainment, learning, and political and social change. Hence, there is a growing interest in understanding information diffusion over online social networks. Because many social interactions currently take place in online networks, social scientists have access to unprecedented amounts of information about social interaction. Prior to the advent of such online networks, these investigations required resource-intensive activities such as random trials, surveys, and manual data collection to gather even small data sets. Now, massive amounts of information about social networks and social interactions are recorded. This wealth of data can allow social scientists to study social interactions on a scale and at a level of detail that has never before been possible. However, Social scientists are not traditionally trained in techniques to deal with the massive amounts of data produced by online social networks. Computer scientists that specialize in databases and knowledge discovery have experience with querying and analyzing enormous amounts of data in a scalable fashion, but they may not be aware of the types of information that are most relevant to understanding social processes. Moreover, it is often necessary to alter theories and models developed in research of traditional social networks to incorporate new features of interactions in online networks, or even develop entirely new models of social processes. To create and validate these new models requires familiarity with social science techniques for modeling of social interactions, as well as knowledge of techniques for modeling and analysis of complex networks that can scale to the size of millions or even billions of users.The project brings together an interdisciplinary team consisting of computer scientists with expertise in databases and data mining, network modeling and analysis, and social media led by Dr. Divyakant Agrawal at the University of California-Santa Barbara to develop computational approaches to model and predict a number of important phenomena in social networks: information diffusion, opinion formation, etc. The team is developing new algorithms and analytical and computational tools that can effectively cope with the massive size of social networks and the data produced within such networks. These tools are being designed account for the complex nature of human behavior by incorporating the spatial, temporal, and relationship-based aspects of social interactions. The long-term goals of this project are to develop tools that help better understand social interactions in online networks, to develop reliable and scalable models to predict the outcomes of such social processes, and to create applications that can shape such outcomes. The project advances the current state of the art in: Querying and analysis of massive datasets, Modeling and analysis of complex networks, and Analysis of social media and social interactions, including in particular, the interplay between multiple simultaneous information diffusion processes. This is a high-risk, potentially high payoff research effort due in part to the challenges associated with obtaining and analyzing data from social networks and social media. In order to model social network entities and interactions, the research team needs access to datasets from online social networks to build, verify and validate models. Similarly, discovering information, knowledge, and user behavior in online social networks require access to social network datasets. In general, acquiring such datasets is a significant challenge due to privacy issues and the proprietary nature of many social network sites. This Early Concept Grant for Exploratory Research (EAGER) project provides a rich set of data repository and evaluation metrics for conducting large-scale investigations involving social networks and social media. The project addresses the data challenge by assembling large data sets from weblog postings and Twitter messages from millions of users, as well as appropriately anonymized data that capture the interactions between participants in social networks such as Facebook. The broad dissemination of the resulting datasets and tools will lower the barrier to (and reduce the risk associated with) entry into Social Informatics and Computational Social Sciences for researchers with diverse backgrounds and expertise. The resulting datasets and tools are likely to stimulate fundamental advances in several subdisciplines within Computer Science including algorithm design, network modeling and analysis, and data mining and knowledge discovery (among others). The project provides unique opportunities for broadening the participation of underrepresented minorities and women in Computer and Information Sciences, and especially those motivated by real-world applications in social sciences (e.g., understanding social interactions). The results of the project will be disseminated through the project web pages at http://cs.ucsb.edu/~dsl/?q=content/data-driven-framework-analyzing-user-interactions-social-media.

社交网络在全球拥有数亿用户，为社交互动、娱乐、学习以及政治和社会变革提供了难以置信的机会。因此，人们对理解在线社交网络上的信息传播越来越感兴趣。由于目前许多社会互动都发生在在线网络中，社会科学家可以获得前所未有的大量关于社会互动的信息。在这种在线网络出现之前，这些调查需要资源密集型活动，如随机试验、调查和手动数据收集，以收集即使是小数据集。现在，大量关于社交网络和社交互动的信息被记录下来。这些丰富的数据可以让社会科学家在以前从未有过的规模和细节水平上研究社会互动。然而，社会科学家传统上并没有接受过处理在线社交网络产生的大量数据的技术培训。专注于数据库和知识发现的计算机科学家拥有以可扩展的方式查询和分析大量数据的经验，但他们可能不知道与理解社会过程最相关的信息类型。此外，经常需要改变传统社交网络研究中开发的理论和模型，以纳入在线网络中交互的新特征，甚至开发全新的社交过程模型。为了创建和验证这些新模型，需要熟悉用于社会互动建模的社会科学技术，以及可以扩展到数百万甚至数十亿用户规模的复杂网络建模和分析技术的知识。该项目汇集了一个跨学科团队，由具有数据库和数据挖掘，网络建模和分析，Divyakant Agrawal博士在加州大学圣巴巴拉分校领导的研究小组开发了计算方法来模拟和预测社交网络中的一些重要现象：信息传播，舆论形成，该团队正在开发新的算法以及分析和计算工具，这些工具可以有效地科普社交网络的巨大规模和数据在这样的网络中产生。这些工具的设计考虑到了人类行为的复杂性，将社会互动的空间、时间和基于关系的方面结合起来。该项目的长期目标是开发工具，帮助更好地了解在线网络中的社交互动，开发可靠和可扩展的模型来预测这些社交过程的结果，并创建可以塑造这些结果的应用程序。该项目在以下方面推进了当前的技术水平：海量数据集的查询和分析，复杂网络的建模和分析，以及社交媒体和社交互动的分析，特别是包括多个同步信息传播过程之间的相互作用。这是一项高风险、潜在高回报的研究工作，部分原因是与从社交网络和社交媒体获取和分析数据相关的挑战。为了对社交网络实体和交互进行建模，研究团队需要访问来自在线社交网络的数据集，以构建、验证和验证模型。类似地，发现在线社交网络中的信息、知识和用户行为需要访问社交网络数据集。一般来说，由于隐私问题和许多社交网站的专有性质，获取此类数据集是一项重大挑战。这个探索性研究早期概念资助（EAGER）项目提供了一套丰富的数据存储库和评估指标，用于进行涉及社交网络和社交媒体的大规模调查。该项目通过收集来自数百万用户的博客帖子和Twitter消息的大型数据集以及适当匿名的数据来应对数据挑战，这些数据收集了Facebook等社交网络参与者之间的互动。由此产生的数据集和工具的广泛传播将降低具有不同背景和专业知识的研究人员进入社会信息学和计算社会科学的障碍（并降低相关风险）。由此产生的数据集和工具可能会刺激计算机科学中的几个子学科的根本性进步，包括算法设计，网络建模和分析，数据挖掘和知识发现等。该项目为扩大代表性不足的少数民族和妇女参与计算机和信息科学提供了独特的机会，特别是那些受到社会科学实际应用激励的少数民族和妇女（例如，理解社会互动）。该项目的结果将通过项目网页http://cs.ucsb.edu/~dsl/?传播q=内容/数据驱动框架分析用户交互社交媒体。