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

Financial news content publishing on youtube.com

Recently a number of academic publications have investigated various properties and dynamics of author's contributors on online Web 2.0 communities. The most intensively examined communities have until now been discussion boards and blogs. In this paper we look at, and identify revealing patterns of content publishers on YouTube. This is the most popular community based service on the world wide web as ranked by the frequency of visited webpages. The research is motivated in terms of gaining better understanding of content publishing habits within financial news topic specifically and we investigate the potential for trend detection in financial markets area. A number of side issues, such as publisher bias and various video properties are also examined. It turns out, the number of video submissions by professional authors has increased considerably in recent months. There is significant quantity to allow statistical analysis of events. As we show, the potential value of the data on YouTube cannot be ignored any longer. We present recent results and work on a project that investigated a completely new dataset, not really considered in previous literature