Detection of News Feeds Items Appropriate for Children

  • Tamara Polajnar
  • Richard Glassey
  • Leif Azzopardi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7224)


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.


Readability Measure Readability Feature Open Directory Project News Feed Negative Document 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tamara Polajnar
    • 1
  • Richard Glassey
    • 1
  • Leif Azzopardi
    • 1
  1. 1.School of Computing ScienceUniversity of GlasgowGlasgowUK

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