社会化媒体的环境扫描与情报分析
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摘要
环境扫描是组织在战略管理中获取外界信息的一种方法。环境扫描可以辅助管理者识别组织外部环境中潜在的风险和机会，并迅速响应外部环境中的变化，从而使组织获得竞争优势。如何从海量的信息中发掘并评估与自身企业或政府组织相关的信息，是环境扫描成功与否的关键。随着Web2.0技术的迅猛发展，社会化媒体(如网络论坛、博客、播客和社交网络等)已经成为人们沟通交流的重要平台。社会化媒体中蕴含着大量用户产生的内容，这些内容既包括有价值的最新资讯又聚集着广大网民的观点和经验。因此，对社会化媒体进行环境扫描，进而辅助组织获取战略决策相关的情报具有重要意义。
本文以管理信息系统领域中设计科学研究为范式，设计并开发商务智能组件从而满足社会化媒体环境扫描与情报分析的需求。本文的研究目标是：(1)提出表征社会化媒体信息的特征提取方法和特征选择方法；(2)设计数据挖掘算法，从海量的社会化媒体数据中提取有效信息；(3)构建数据分析模型，评估社会化媒体中情报的有用性。本文的主要研究对象是社会化媒体中的文本内容，我们着重分析文本内容中所蕴含的话题、情感、事件、写作风格以及交互模式等信息。
本文分为5个部分。第1章首先介绍研究背景，在系统地分析国内外研究现状的基础上，明确提出本文的研究问题。第2章以信息获取理论为核心，提出社会化媒体环境扫描的研究框架，支持决策者动态地组织、开发、以及选择与决策过程需求相匹配的信息。依据第2章提出的研究设计方案，从第3章到第5章分别提出支持社会化媒体环境扫描与情报分析的相关技术和方法。其中，第3章提出面向社会化媒体环境扫描信息需求的文本特征提取方法，将基于文本内容和领域知识的特征相结合，刻画社会化媒体中所蕴藏的丰富信息。第4章提出面向社会化媒体环境扫描的信息收集方法，该方法基于半监督式学习的文本分类技术，支持从海量的社会化媒体数据中提取与决策相关的有效信息。第5章对社会化媒体中情报的有用性进行分析，并着重剖析影响消费者对顾客评论感知有用性的因素。
本文的创新点可以概括为以下四个方面。第一，设计了支持社会化媒体环境扫描的系统模型。该模型可以有效地支持组织的环境扫描过程，包括信息收集、信息过滤、以及信息使用。第二，提出了一种自动获得领域知识文本特征的提取方法。这种特征提取方法可以有效地增强机器学习算法在文本分析中的性能。第三，提出了一种基于半监督式机器学习的文本分类算法。该方法能够有效地对社会化媒体中的多主题文本内容进行分类。第四，构建了对顾客评论进行有用性分析的理论模型。该模型揭示了消费者对顾客评论感知质量的前因。这对在线零售商了解商品评论在消费者购买决策过程中的不同阶段所发挥的作用具有重要理论意义；同时为在线零售商开展口碑营销提供了实际参考。
Environmental scanning is an approach an organization uses to monitor itsexternal environment including types of strategic external information shared andacted upon within the organization. Environmental scanning helps an organizationadapt to changing external circumstance, and can provide ‘signals’ for identifyingthreats and opportunities and gaining competitive advantages. The ability to assessthe relevance of topics and related sources in information-rich environments iscritical to organization success when scanning business enviroments. With theadvent of Web2.0, the past few years have witnessed the rapid rise of social mediasystems, such as Web forums, blogs, wikis, and media-sharing websites. Socialmedia systems contain large volumes of user generated content in various forms,from plain text to rich multi-media. With the availability of vast quantities ofinformation in social media, an organization has a great need for an automatedmethodology to scan and use this information.
This dissrtation follows the design science research paradigm in MIS, byaddressing issues pertaining to the design and development of an important ITartifact capable of meeting the challenges of environmental scanning in social media.The purpose of this dissertation is to provide an understanding of the social mediaon strategy planning and knowledge management; to learn whether using datamining and social media analytics can yield better assimilation of knowledgemanagement in organization. Using Information Foraging Theory (IFT) as a kerneltheory, emphasis is placed on developing techniques for analyzing textual andideational information. A rich set of features are utilized to represent textual (e.g.,style, genres, social cues etc.) and ideational (topics, sentiments, affects, etc)information. The research revolves around a core set of algorithms used for featureselection, categorization, and analysis of textual content from social media.
The dissertation is arranged in five chapters. Chapter1of this dissertationprovides an introduction to the environmental scanning problem in light of thecurrent problem of gathering and using the huge volume of information available viathe social media. In Chapter2, we propose an innovation research framework basedon IFT, supporting an active organization, exploration and selection of informationthat matches the needs of decision-makers in information processing. Chapter3relates to the textual feature extraction associated with social media analytics. Weassess different textual features to gauge radical opinions using machine learningtechniques on the messages from hate group Web forums. Chapter4comprises adetailed description in the design, development, and evaluation of a system that can endorse the managerial information gathering and filtering from social media.Experiments are conducted on Web forum messages in the domain of customercomplaint identification using information retrieval and partially supervised leaningtechniques. Chapter5is concerned with the information helpfulness evaluation fromsocial media, supporting the effective use of information in environmental scanning.Drawing on the paradigm of search and experience goods from informationeconomics, we develop and test a model of customer review helpfulness.
The contributions of this dissertation can be manifested into four folds. First,we design an intergrated social media intelligent system that contributes to theeffective environmental scanning. The system supports the crucial tasks of theenvironmentcal scanning process, including information gathering, informationfiltering, and information use. Second, we present a new framework for automaticacquisition of domain-specific knowledge in social media analytics. Our approachenhances machine learning algorithms with features generated from domain-specificknowledge. This knowledge is represented by ontologies that contain hundreds ofthousands of concepts, further enriched through controlled Web crawling. Third, wepropose a novel labeling heuristic to extract high-quality content from social media.Our approach can dynamically capture the characteristics of the positive class withdiverse topics. Finally, we provide a theoretical framework to understand the contextof online reviews. Our study explore the antecedents of perceived quality of onlinecustomer reviews. Our findings can increase online retailers’ understanding of therole online reviews play in the multiple stages of the consumer’s purchase decisionprocess. The results of this study can be used to develop guidelines forword-of-mouth marketing.
本文以管理信息系统领域中设计科学研究为范式，设计并开发商务智能组件从而满足社会化媒体环境扫描与情报分析的需求。本文的研究目标是：(1)提出表征社会化媒体信息的特征提取方法和特征选择方法；(2)设计数据挖掘算法，从海量的社会化媒体数据中提取有效信息；(3)构建数据分析模型，评估社会化媒体中情报的有用性。本文的主要研究对象是社会化媒体中的文本内容，我们着重分析文本内容中所蕴含的话题、情感、事件、写作风格以及交互模式等信息。
本文分为5个部分。第1章首先介绍研究背景，在系统地分析国内外研究现状的基础上，明确提出本文的研究问题。第2章以信息获取理论为核心，提出社会化媒体环境扫描的研究框架，支持决策者动态地组织、开发、以及选择与决策过程需求相匹配的信息。依据第2章提出的研究设计方案，从第3章到第5章分别提出支持社会化媒体环境扫描与情报分析的相关技术和方法。其中，第3章提出面向社会化媒体环境扫描信息需求的文本特征提取方法，将基于文本内容和领域知识的特征相结合，刻画社会化媒体中所蕴藏的丰富信息。第4章提出面向社会化媒体环境扫描的信息收集方法，该方法基于半监督式学习的文本分类技术，支持从海量的社会化媒体数据中提取与决策相关的有效信息。第5章对社会化媒体中情报的有用性进行分析，并着重剖析影响消费者对顾客评论感知有用性的因素。
本文的创新点可以概括为以下四个方面。第一，设计了支持社会化媒体环境扫描的系统模型。该模型可以有效地支持组织的环境扫描过程，包括信息收集、信息过滤、以及信息使用。第二，提出了一种自动获得领域知识文本特征的提取方法。这种特征提取方法可以有效地增强机器学习算法在文本分析中的性能。第三，提出了一种基于半监督式机器学习的文本分类算法。该方法能够有效地对社会化媒体中的多主题文本内容进行分类。第四，构建了对顾客评论进行有用性分析的理论模型。该模型揭示了消费者对顾客评论感知质量的前因。这对在线零售商了解商品评论在消费者购买决策过程中的不同阶段所发挥的作用具有重要理论意义；同时为在线零售商开展口碑营销提供了实际参考。
Environmental scanning is an approach an organization uses to monitor itsexternal environment including types of strategic external information shared andacted upon within the organization. Environmental scanning helps an organizationadapt to changing external circumstance, and can provide ‘signals’ for identifyingthreats and opportunities and gaining competitive advantages. The ability to assessthe relevance of topics and related sources in information-rich environments iscritical to organization success when scanning business enviroments. With theadvent of Web2.0, the past few years have witnessed the rapid rise of social mediasystems, such as Web forums, blogs, wikis, and media-sharing websites. Socialmedia systems contain large volumes of user generated content in various forms,from plain text to rich multi-media. With the availability of vast quantities ofinformation in social media, an organization has a great need for an automatedmethodology to scan and use this information.
This dissrtation follows the design science research paradigm in MIS, byaddressing issues pertaining to the design and development of an important ITartifact capable of meeting the challenges of environmental scanning in social media.The purpose of this dissertation is to provide an understanding of the social mediaon strategy planning and knowledge management; to learn whether using datamining and social media analytics can yield better assimilation of knowledgemanagement in organization. Using Information Foraging Theory (IFT) as a kerneltheory, emphasis is placed on developing techniques for analyzing textual andideational information. A rich set of features are utilized to represent textual (e.g.,style, genres, social cues etc.) and ideational (topics, sentiments, affects, etc)information. The research revolves around a core set of algorithms used for featureselection, categorization, and analysis of textual content from social media.
The dissertation is arranged in five chapters. Chapter1of this dissertationprovides an introduction to the environmental scanning problem in light of thecurrent problem of gathering and using the huge volume of information available viathe social media. In Chapter2, we propose an innovation research framework basedon IFT, supporting an active organization, exploration and selection of informationthat matches the needs of decision-makers in information processing. Chapter3relates to the textual feature extraction associated with social media analytics. Weassess different textual features to gauge radical opinions using machine learningtechniques on the messages from hate group Web forums. Chapter4comprises adetailed description in the design, development, and evaluation of a system that can endorse the managerial information gathering and filtering from social media.Experiments are conducted on Web forum messages in the domain of customercomplaint identification using information retrieval and partially supervised leaningtechniques. Chapter5is concerned with the information helpfulness evaluation fromsocial media, supporting the effective use of information in environmental scanning.Drawing on the paradigm of search and experience goods from informationeconomics, we develop and test a model of customer review helpfulness.
The contributions of this dissertation can be manifested into four folds. First,we design an intergrated social media intelligent system that contributes to theeffective environmental scanning. The system supports the crucial tasks of theenvironmentcal scanning process, including information gathering, informationfiltering, and information use. Second, we present a new framework for automaticacquisition of domain-specific knowledge in social media analytics. Our approachenhances machine learning algorithms with features generated from domain-specificknowledge. This knowledge is represented by ontologies that contain hundreds ofthousands of concepts, further enriched through controlled Web crawling. Third, wepropose a novel labeling heuristic to extract high-quality content from social media.Our approach can dynamically capture the characteristics of the positive class withdiverse topics. Finally, we provide a theoretical framework to understand the contextof online reviews. Our study explore the antecedents of perceived quality of onlinecustomer reviews. Our findings can increase online retailers’ understanding of therole online reviews play in the multiple stages of the consumer’s purchase decisionprocess. The results of this study can be used to develop guidelines forword-of-mouth marketing.
引文
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