A treatise on Web 2.0 with a case study from the financial markets

There has been much hype in vocational and academic circles surrounding the emergence of web 2.0 or social media; however, relatively little work was dedicated to substantiating the actual concept of web 2.0. Many have dismissed it as not deserving of this new title, since the term web 2.0 assumes a certain interpretation of web history, including enough progress in certain direction to trigger a succession [i.e. web 1.0 → web 2.0]. Others provided arguments in support of this development, and there has been a considerable amount of enthusiasm in the literature. Much research has been busy evaluating current use of web 2.0, and analysis of the user generated content, but an objective and thorough assessment of what web 2.0 really stands for has been to a large extent overlooked. More recently the idea of collective intelligence facilitated via web 2.0, and its potential applications have raised interest with researchers, yet a more unified approach and work in the area of collective intelligence is needed. This thesis identifies and critically evaluates a wider context for the web 2.0 environment, and what caused it to emerge; providing a rich literature review on the topic, a review of existing taxonomies, a quantitative and qualitative evaluation of the concept itself, an investigation of the collective intelligence potential that emerges from application usage. Finally, a framework for harnessing collective intelligence in a more systematic manner is proposed. In addition to the presented results, novel methodologies are also introduced throughout this work. In order to provide interesting insight but also to illustrate analysis, a case study of the recent financial crisis is considered. Some interesting results relating to the crisis are revealed within user generated content data, and relevant issues are discussed where appropriate

Power of Web 2.0 mass collaboration in computational intelligence and its’ uses, an example from finance

One of the main issues of concern within world wide web is the emergence of web 2.0 mass collaboration systems and our understanding of this new phenomenon. Web 2.0 systems have gained enormous popularity in recent years, however as is often case with novel technologies, the real merits sometime stay somewhat obscured to many researchers. In this short paper web 2.0 applications are lightly introduced, with parallels to computational intelligence being drawn and some experimental results from the financial markets presented, to illustrate value of web 2.0. This paper highlights a number of important issues that deserve academic attention. We hope this paper will serve as a light and not too technical introduction towards encouraging others in computational intelligence to consider leveraging web 2.0

Media sharing websites and the US financial markets

Recently, one of the main issues of concern within the world wide web is the understanding of web 2.0 mass collaboration systems. These systems have emerged in recent years and gained enormous popularity. It must, however, be pointed out, that the potential and practical application of web 2.0 are still not well understood and deserve academic attention. In this paper we investigate the online media sharing collaborative community and its applications for uses in stock market analysis and prediction. Specifically, we look at Youtube.com, one of the most popular social media sharing websites. The association with stock market behaviour and usage patterns are investigated. This work became of more interest and significance with the recent credit crunch crisis. The data under investigation is novel, and to our knowledge, this paper reports the first investigation of its kind to the use of collaborative media sharing website for stock market analysis. We find significant association between video meta-data and textual data using a content driven sentiment text mining approach. The results are very encouraging and importantly highlight efficient information transfer to online media sharing communities as there seems to be predictive value in youtube data