Abstract
Identifying child-appropriate web content is an important yet difficult classification task. This novel task is characterised by attempting to determine age/child appropriateness (which is not necessarily topic-based), despite the presence of unbalanced class sizes and the lack of quality training data with human judgements of appropriateness. Classification of feeds, a subset of web content, presents further challenges due to their temporal nature and short document format. In this paper, we discuss these challenges and present baseline results for this task through an empirical study that classifies incoming news stories as appropriate (or not) for children. We show that while the naïve Bayes approach produces a higher AUC it is vulnerable to the imbalanced data problem, and that support vector machine provides a more robust overall solution. Our research shows that classifying children’s content is a non-trivial task that has greater complexities than standard text based classification. While the F-score values are consistent with other research examining age-appropriate text classification, we introduce a new problem with a new dataset.
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Bilal, D., Sarangthem, S., Bachir, I.: Toward a model of children’s information seeking behavior in using digital libraries. In: IIiX 2008, pp. 145–151. ACM, NY (2008)
Bouckaert, R.R., Frank, E.: Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 3–12. Springer, Heidelberg (2004), http://dx.doi.org/10.1007/978-3-540-24775-3%5f3
Druin, A., Foss, E., Hutchinson, H., Golub, E., Hatley, L.: Children’s roles using keyword search interfaces at home. In: CHI 2010, pp. 413–422. ACM, New York (2010)
Eickhoff, C., Serdyukov, P., de Vries, A.P.: A combined topical/non-topical approach to identifying web sites for children. In: 4th International Conference on Web Search and Data Mining (WSDM). ACM, Hong Kong (2011)
Glassey, R., Elliott, D., Polajnar, T., Azzopardi, L.: Interaction-based information filtering for children. In: Proceeding of the Third Symposium on Information Interaction in Context, IIiX 2010, pp. 329–334. ACM, NY (2010), http://doi.acm.org/10.1145/1840784.1840834
Gyllstrom, K., Moens, M.F.: A picture is worth a thousand search results: finding child-oriented multimedia results with collage. In: SIGIR, pp. 731–732 (2010)
Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Joachims, T.: Making large-Scale SVM Learning Practical. In: Advances in Kernel Methods - Support Vector Learning. MIT-Press, Cambridge (1999)
Large, A., Nesset, V., Beheshti, J.: Children as information seekers: what researchers tell us. New Review of Childrens Literature and Librarianship 14(2), 121–140 (2008)
Large, A., Beheshti, J., Tabatabaei, N., Nesset, V.: Developing a visual taxonomy: Children’s views on aesthetics. J. Am. Soc. Inf. Sci. Technol. 60(9), 1808–1822 (2009)
Lewis, D.D.: Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 4–15. Springer, Heidelberg (1998)
Nielsen, J.: Usability of websites for children: Design guidelines for targeting users aged 3-12 years. Web publication (September 2010), http://www.nngroup.com/reports/kids/
Ofcom: UK children’s media literacy (March 2010), http://stakeholders.ofcom.org.uk/market-data-research/media-literacy/medlitpub/medlitpubrss/ukchildrensml/
Rideout, V.J., Foeh, U.G., Roberts, D.F.: Report: Generation m2: Media in the lives of 8- to 18-year-olds. Tech. rep., Kaiser Family Foundation (January 2010), http://www.kff.org/entmedia/upload/8010.pdf
Schwarm, S.E., Ostendorf, M.: Reading level assessment using support vector machines and statistical language models. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL 2005, pp. 523–530. ACL, Ann Arbour (2005), http://dx.doi.org/10.3115/1219840.1219905
Smith, E., Senter, R.: Automated readability index. Tech. Rep. AMRL-TR-66-220, Cincinnati University, Ohio (1967)
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Polajnar, T., Glassey, R., Azzopardi, L. (2012). Detection of News Feeds Items Appropriate for Children. In: Baeza-Yates, R., et al. Advances in Information Retrieval. ECIR 2012. Lecture Notes in Computer Science, vol 7224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28997-2_6
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DOI: https://doi.org/10.1007/978-3-642-28997-2_6
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