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Issue No.06 - November/December (2010 vol.14)
pp: 47-55
Jussara M. Almeida , Universidade Federal de Minas Gerais, Brazil
Marcos André Gonçalves , Universidade Federal de Minas Gerais, Brazil
Flavio Figueiredo , Universidade Federal de Minas Gerais, Brazil
Henrique Pinto , Universidade Federal de Minas Gerais, Brazil
Fabiano Belem , Universidade Federal de Minas Gerais, Brazil
Most Web 2.0 applications let users associate textual information with multimedia content. Despite each application's lack of editorial control, these textual features are still the primary source of information for many relevant services such as search. Previous efforts in assessing the quality of these features target, mostly, single applications, and mainly focus on tags, thus neglecting the potential of other features. The current study assesses and compares the quality of four textual features (title, tags, description, and comments) for supporting information services using data from YouTube, YahooVideo, and LastFM.
Internet computing, information quality, metadata, Web 2.0 services, YouTube, YahooVideo, LastFM
Jussara M. Almeida, Marcos André Gonçalves, Flavio Figueiredo, Henrique Pinto, Fabiano Belem, "On the Quality of Information for Web 2.0 Services", IEEE Internet Computing, vol.14, no. 6, pp. 47-55, November/December 2010, doi:10.1109/MIC.2010.102
1. S. Boll, "MultiTube — Where Web 2.0 and Multimedia Could Meet," IEEE MultiMedia, vol. 14, no. 1, 2007, pp. 9–13.
2. D. Ramage et al., "Clustering the Tagged Web," Proc. 2nd ACM Int'l Conf. Web Search and Data Mining, ACM Press, 2009, pp. 54–63.
3. R. Schenkel et al., "Efficient Top-k Querying Over Social Tagging Networks," Proc. 31st Ann. Int'l ACM Conf. Research and Development in Information Retrieval, ACM Press, 2008, pp. 523–530.
4. B. Sigurbjornsson and R. van Zwol,"Flickr Tag Recommendation Based on Collective Knowledge," Proc. 17th Int'l World Wide Web Conf., ACM Press, 2008, pp. 327–336.
5. K. Bischoff et al., "Can All Tags Be Used for Search?" Proc. 17th ACM Conf. Information and Knowledge Management, ACM Press, 2008, pp. 193–202.
6. C. Marshall, "No Bull, No Spin: A Comparison of Tags with Other Forms of User Metadata," Proc. 9th ACM/IEEE CS Joint Conf. Digital Libraries, ACM Press, 2009, pp. 241–250.
7. P. Heymann, G. Koutrika, and H. Garcia-Molina, "Fighting Spam on Social Web Sites: A Survey of Approaches and Future Challenges," IEEE Internet Computing, vol. 14, no. 6, 2007, pp. 36–45.
8. F. Figueiredo et al., "Evidence of Quality of Textual Features on the Web 2.0," Proc. 18th ACM Conf. Information and Knowledge Management, ACM Press, 2009, pp. 909–918.
9. T. Mitchell, Machine Learning, McGraw-Hill, 1997.
10. D. Fernandes et al., "Computing Block Importance for Searching on Web Sites," Proc. 16th ACM Conf. Information and Knowledge Management, ACM Press, 2007, pp. 165–174.
11. J.L. Rodgers and W. Nicewander, "Thirteen Ways to Look at the Correlation Coefficient," The American Statistician, vol. 42, no. 1, 1988, pp. 59–66.
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