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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/6423

Title: Media sharing websites and the US financial markets
Authors: Sykora, Martin D.
Panek, Marek
Keywords: Data mining
Information retrieval
Web 2.0
Mass collaboration
Finance
News publishing
Issue Date: 2009
Publisher: International Association for Development of the Information Society (IADIS)
Citation: SYKORA, M.D. and PANEK, M., 2009. Media sharing websites and the US financial markets. IN: Isaías, P., White, B. and Nune, M.B. (eds). IADIS International Conference WWW/Internet 2009, Rome, Italy, 19-22 November 2009, pp.197-204.
Abstract: 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.
Description: This paper was presenetd at the IADIS International Conference WWW/Internet: http://www.internet-conf.org/2009/
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/6423
ISBN: 9789728924935
Appears in Collections:Conference Papers and Presentations (Computer Science)

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