30th August 2008 - Mining the Social Web - Web Mining for Web2.0

On 28th and 29th August I had the privilege of attending the course "Mining the Social Web - Web Mining for Web2.0" by Jaideep Srivastava. The course was a great inspiration for me, and it opened my eyes for new exciting approaches to using the data gathered in the world of Web2.0.

In particular, Jaideep’s introduction of Google Trends gave me a few ideas. In the course Jaideep mentioned Google Trends as one of the best approaches to taking the temperature on a product or a brand, or as an approach to understanding people’s interests in a geographical area. Upon testing it after the course, there is no doubt in my mind that Google Trends could be a very relevant data provider in my Sentinel Research. Specifically, I think that an experiment with applying Sentinels to Google Trends data in my proposed article "Sentinel Discovery in the Real-World" would be highly interesting. Such an approach would give marketers a real early warning and allow them to act intelligently based on changes in brand/product "interestingness".

Another thought that came up when reading the paper "Modularity and community structure in networks", which was part of the course, was the idea to use the same approach to reduce the number of human interactions (the number of clicks!) in an application. This could be done by using specific functionality in an application as nodes in a network graph, and the click-streams that a user travels through the application would be the edges in the graph. Using the approach from the paper to identify community structures one could identify clusters of functionality that is "naturally" grouped; and by mining such knowledge and adding it to the application, a new user-interface can be made which guides the users faster to a desired result. These thoughts relate directly to my research about improving the speed in the OODA loop in Business Intelligence applications.

Overall I enjoyed the course very much, and I think that one of the biggest eye-openers for me was the fact the techniques proposed in the course are by no means exclusively related to dedicated social networks such as Facebook or LinkedIn. The techniques can by applied to other data structures as well, and a data structure can also be interpreted in order to grow a network: Therefore, the techniques are really applicable and relevant in a great number of scenario where cascading behavior across a number of interrelated entities is to be understood, simulated or analyzed.

All in all, a great course with lots of ideas and suggestions for new research as well as real-world applications.

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