A series of case studies to enhance the social utility of RSS

RSS (really simple syndication, rich site summary or RDF site summary) is a dialect of XML that provides a method of syndicating on-line content, where postings consist of frequently updated news items, blog entries and multimedia. RSS feeds, produced by organisations or individuals, are often aggregated, and delivered to users for consumption via readers. The semi-structured format of RSS also allows the delivery/exchange of machine-readable content between different platforms and systems. Articles on web pages frequently include icons that represent social media services which facilitate social data. Amongst these, RSS feeds deliver data which is typically presented in the journalistic style of headline, story and snapshot(s). Consequently, applications and academic research have employed RSS on this basis. Therefore, within the context of social media, the question arises: can the social function, i.e. utility, of RSS be enhanced by producing from it data which is actionable and effective? This thesis is based upon the hypothesis that the fluctuations in the keyword frequencies present in RSS can be mined to produce actionable and effective data, to enhance the technology's social utility. To this end, we present a series of laboratory-based case studies which demonstrate two novel and logically consistent RSS-mining paradigms. Our first paradigm allows users to define mining rules to mine data from feeds. The second paradigm employs a semi-automated classification of feeds and correlates this with sentiment. We visualise the outputs produced by the case studies for these paradigms, where they can benefit users in real-world scenarios, varying from statistics and trend analysis to mining financial and sporting data. The contributions of this thesis to web engineering and text mining are the demonstration of the proof of concept of our paradigms, through the integration of an array of open-source, third-party products into a coherent and innovative, alpha-version prototype software implemented in a Java JSP/servlet-based web application architecture

A series of case studies to enhance the social utility of RSS

RSS (really simple syndication, rich site summary or RDF site summary) is a dialect of XML that provides a method of syndicating on-line content, where postings consist of frequently updated news items, blog entries and multimedia. RSS feeds, produced by organisations or individuals, are often aggregated, and delivered to users for consumption via readers. The semi-structured format of RSS also allows the delivery/exchange of machine-readable content between different platforms and systems. Articles on web pages frequently include icons that represent social media services which facilitate social data. Amongst these, RSS feeds deliver data which is typically presented in the journalistic style of headline, story and snapshot(s). Consequently, applications and academic research have employed RSS on this basis. Therefore, within the context of social media, the question arises: can the social function, i.e. utility, of RSS be enhanced by producing from it data which is actionable and effective? This thesis is based upon the hypothesis that the fluctuations in the keyword frequencies present in RSS can be mined to produce actionable and effective data, to enhance the technology's social utility. To this end, we present a series of laboratory-based case studies which demonstrate two novel and logically consistent RSS-mining paradigms. Our first paradigm allows users to define mining rules to mine data from feeds. The second paradigm employs a semi-automated classification of feeds and correlates this with sentiment. We visualise the outputs produced by the case studies for these paradigms, where they can benefit users in real-world scenarios, varying from statistics and trend analysis to mining financial and sporting data. The contributions of this thesis to web engineering and text mining are the demonstration of the proof of concept of our paradigms, through the integration of an array of open-source, third-party products into a coherent and innovative, alpha-version prototype software implemented in a Java JSP/servlet-based web application architecture

Assisting blog publication: annotation, model transformation, and crossblogging techniques

Although blogs exist from the beginning of the Internet, their use has considerablybeen increased in the last decade. Nowadays, they are ready for being used bya broad range of people. From teenagers to multinationals, everyone can have aglobal communication space.Companies know blogs are a valuable publicity tool to share information withthe participants, and the importance of creating consumer communities aroundthem: participants come together to exchange ideas, review and recommend newproducts, and even support each other. Also, companies can use blogs for differentpurposes, such as a content management system to manage the content of websites,a bulletin board to support communication and document sharing in teams,an instrument in marketing to communicate with Internet users, or a KnowledgeManagement Tool. However, an increasing number of blog content do not findtheir source in the personal experiences of the writer. Thus, the information cancurrently be kept in the user¿s desktop documents, in the companies¿ catalogues,or in another blogs. Although the gap between blog and data source can be manuallytraversed in a manual coding, this is a cumbersome task that defeats the blog¿seasiness principle. Moreover, depending on the quantity of information and itscharacterisation (i.e., structured content, unstructured content, etc.), an automaticapproach can be more effective.Based on these observations, the aim of this dissertation is to assist blog publicationthrough annotation, model transformation and crossblogging techniques.These techniques have been implemented to give rise to Blogouse, Catablog, andBlogUnion. These tools strive to improve the publication process considering theaforementioned data sources.Ministerio de Educación y Ciencia TIC2002-0144