Michael G. Noll

Applied Research. Big Data. Distributed Systems. Open Source.

Authors vs. Readers: A Comparative Study of Document Metadata and Content in the WWW

My paper “Authors vs. Readers: A Comparative Study of Document Metadata and Content in the WWW” has been accepted for publication at this year’s ACM Symposium on Document Engineering, which will be held at the University of Manitoba, Winnipeg, Canada from August 28 - 31, 2007.

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Abstract

Collaborative tagging describes the process by which many users add metadata in the form of unstructured keywords to shared content. The recent practical success of web services with such a tagging component like Flickr or del.icio.us has provided a plethora of user-supplied metadata about web content for everyone to leverage. In this paper, we conduct a quantitative and qualitative analysis of metadata and information provided by the authors and publishers of web documents compared with metadata supplied by end users for the same content. Our study is based on a random sample of 100,000 web documents from the Open Directory, for which we examined the original documents from the World Wide Web in addition to data retrieved from the social bookmarking service del.icio.us, the content rating system ICRA, and the search engine Google. The data set of our experiments, called DMOZ100k06, is freely available for other research. We hope that the results of our study give researchers valuable insights for building and improving systems for document engineering, retrieval, and classification in the World Wide Web today. Information sources used to build the DMOZ100K06 data set
Information sources used for building the DMOZ100K06 data set.
Note: I used my custom del.icio.us Python API for retrieving the relevant data from del.icio.us.

Paper

You can download the paper as a PDF document:

Presentation

Cover Presentation DocEng 2007 My talk given at the DocEng 2007 conference is available for download (PDF).

Acknowledgements

I would like to thank Alexandre Dulaunoy for his help with mirroring the HTML documents in the data set, and his comments while I was preparing the paper. Again, it’s been greatly appreciated, Alex!

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