Revisiting Spam Filtering in Web Search


The Waterloo spam scores are now a commonly used static document feature in web collections such as ClueWeb. This feature can be used as a post-retrieval filter, as a document prior, or as one of many features in a Learning-to-Rank system. In this work, we highlight the risks associated with using spam scores as a post-retrieval filter, which is now common practice in experiments with the ClueWeb test collection. While it increases the average evaluation score and boosts the performance of some topics, it can significantly harm the performance of others. Through a detailed failure analysis, we show that simple spam filtering is a high risk practice that should be avoided in future work, particularly when working with the ClueWeb12 test collection.

Proceedings of the 23rd Australasian Document Computing Symposium (ADCS 2018)