Accessing, Analyzing, and Extracting Information from User Generated Contents

Accessing, Analyzing, and Extracting Information from User Generated Contents

Paolo Casoto, Antonina Dattolo, Paolo Omero, Nirmala Pudota, Carlo Tasso
DOI: 10.4018/978-1-60566-384-5.ch018
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The concepts of the participative Web, mass collaboration, and collective intelligence grow out of a set of Web methodologies and technologies which improve interaction with users in the development, rating, and distribution of user-generated content. UGC is one of the cornerstones of Web 2.0 and is the core concept of several different kinds of applications. UGC suggests new value chains and business models; it proposes innovative social, cultural, and economic opportunities and impacts. However, several open issues concerning semantic understanding and managing of digital information available on the Web, like information overload, heterogeneity of the available content, and effectiveness of retrieval are still unsolved. The research experiences we present in this chapter, described in literature or achieved in our research laboratory, are aimed at reducing the gap between users and information understanding, by means of collaborative and cognitive filtering, sentiment analysis, information extraction, and knowledge conceptual modeling.
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The Web of the 1990s, identified after as Web 1.0, has been a read-only medium for the majority of users, even if the original idea of Tim Berners-Lee was related to a read-write Web (the first browser, named WorldWideWeb, was also a HTML editor). In 2004, the term Web 2.0 firstly used by Dale Dougherty during a O`Reilly Media brainstorming session has been defined by Tim O`Reilly (2007) as “the business revolution in the computer industry caused by the move to the Internet as platform, and an attempt to understand the rules for success on that new platform”. Web 2.0 is characterized by active participation and interaction of users that become Web's authors and can directly create, express themselves and communicate.

The innovative approach represented by Web 2.0 is only marginally related with the availability of a real technological advance in intercommunication technologies, it represents rather a new way of thinking, a new business opportunity that makes it very simple to create and share contents online and transforms every individual user of the Web into a potential producer; in this way, users may express themselves through User-Generated Content (UGC). Examples of UGC range from social bookmarking (e.g., to photo and video sharing (e. g., Flickr and YouTube), from social networking sites (e.g., Myspace, Friendster, Facebook) to virtual world content (e.g., Second Life), from wikis (e.g., Wikipedia) to social-media blogs (e.g., BoingBoing, Engadget) and podcasting.

Web 2.0 changed, in the last few years, the vision of both personal and commercial websites, moving from large, closed and centralized repositories of static information to dynamic aggregators of heterogeneous contents, integrated into the Internet platform. This trend has been confirmed by the ever growing amount of API users can adopt to integrate their own applications and sites with the most important Web 2.0 applications, like, for example, YouTube or Flickr, implementing the so-called architecture of participation, where user interaction is encouraged in order to add value to the application itself.

Users can be effectively part of the development of Web 2.0 applications, by identifying the set of required features and validating the yet implemented ones, reducing the life cycle of applications and improving their usability, in a development approach known as perpetual beta.

Users interaction with Web 2.0 applications is exploited by Web services developers and providers because it also allows enriching the application contents by means of harnessing collective intelligence expressed by users. Tim O`Reilly (2007) shows how some of the most successful applications, which survived the transition between Web 1.0 and Web 2.0, are all characterized by a common property: the integration of users collective intelligence into their information flow. In particular the author presents the cases of Amazon, which obtained most of its success thanks to the books reviews written by users, and Google, whose ranking criteria, PageRank, is strongly based on the assumption that people used to link at most, in their personal websites, interesting and trusted documents.

Key Terms in this Chapter

Collective Intelligence: Natural product of the independent opinions or behaviors of diverse individuals or groups in a decentralized system (flock, market, guessing game) that aggregates those opinions or behaviors. It is the intelligence of a collective, which arises from one or more sources

User Generated Content (UGC): UGC refers to various kinds of media content, publicly available, that are produced by end-users. It reflects the expansion of media production through new technologies that are accessible and affordable to the general public

Cognitive Filtering: Technique in which the description of a document is matched against a user profile where descriptions relate to static autonomous properties.

Business Intelligence: Broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions

Semantic Web: Abstract representation of data on the World Wide Web, based on the RDF standards. It is an extension of the current Web that provides an easier way to find, share, reuse and combine information more easily

these include digital video: blogging, podcasting, news, gossip, research, mobile phone photography and wikis. In addition to these technologies, user generated content may also employ a combination of open source, free software, and flexible licensing or related agreements to further diminish the barriers to collaboration, skill-building and discovery

Folksonomies: Contraction of folk (person) and taxonomy, a folksonomy is a decentralized, social approach to creating classification data (metadata)

Opinion Mining (Sentiment Mining, Opinion/Sentiment Extraction): Area of research that attempts to make automatic systems to determine human opinion from text written in natural language

Ontology: An ontology is a collection of concepts and relations among them, based on the principles of classes, identified by categories, properties that are different aspects of the class and instances that are the things

Information Extraction: The act of automatically extracting structured information, i.e. categorized and contextually and semantically well-defined data, from unstructured machine-readable documents

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