"Is all that talk just noise? Predicting the future through social media"
Ever since the launch of Twitter, the information stream on this platform has grown exponentially. The site is no longer used only for personal status updates, but represents the fastest way to distribute new and valuable information. In the context of the social semantic web, there are numerous attempts to aggregate this information in a meaningful fashion.
Last year, a group at the TUM School of Management has launched the research project TUitter. As part of this project, we investigate in how far the information content on Twitter can be used as an indicator of events in the offline world. In the context of the German federal elections, we have already shown that the information content of Twitter messages can serve to predict election results and reflect the political landscape surprisingly well (for details, read the paper or watch the presentation at the International Conference on Weblogs and Social Media, which took place in Washington in May). Initial results to use Twitter as an indicator of financial market activity are equally promising. As part of this analysis we are using machine learning techniques to classify message board content automatically and extract the sentiment contained in the postings.
Last year, a group at the TUM School of Management has launched the research project TUitter. As part of this project, we investigate in how far the information content on Twitter can be used as an indicator of events in the offline world. In the context of the German federal elections, we have already shown that the information content of Twitter messages can serve to predict election results and reflect the political landscape surprisingly well (for details, read the paper or watch the presentation at the International Conference on Weblogs and Social Media, which took place in Washington in May). Initial results to use Twitter as an indicator of financial market activity are equally promising. As part of this analysis we are using machine learning techniques to classify message board content automatically and extract the sentiment contained in the postings.
Currently we are transferring these research results into an online application that harnesses this "wisdom of crowds" and may help aggregate stock-related financial information. It provides the online community with an innovative approach to generate fresh trading ideas and investment advice.
