Motivation for hyperlink creation using inter-page relationships
This work addresses the need for efficient hyperlink analysis in webometrics research, though it is incremental as it applies an existing machine learning method to a specific domain.
The paper tackled the problem of unreliable raw hyperlink counts in webometrics by automating the classification of hyperlink motivations in UK academic websites using decision tree induction, achieving a speed-up that makes large-scale studies feasible.
Using raw hyperlink counts for webometrics research has been shown to be unreliable and researchers have looked for alternatives. One alternative is classifying hyperlinks in a website based on the motivation behind the hyperlink creation. The method used for this type of classification involves manually visiting a webpage and then classifying individual links on the webpage. This is time consuming, making it infeasible for large scale studies. This paper speeds up the classification of hyperlinks in UK academic websites by using a machine learning technique, decision tree induction, to group web pages found in UK academic websites into one of eight categories and then infer the motivation for the creation of a hyperlink in a webpage based on the linking pattern of the category the webpage belongs to.