Predicting event-relatedness of popular queries

Abstract

Many but not all popular queries are related to ongoing or recent events. In this paper, we identify 20 features including both contextual and temporal features from a small set of search results of a query and predict its event-relatedness. Search results from news and blog search engines are evaluated. Our analysis shows that the number of named entities in search results and their appearances in Wikipedia are among the most discriminating features for query event-relatedness prediction. Our study also shows that contextual features are more effective than temporal features. Evaluated with four classifiers (i.e., Support Vector Machine, Naive Bayes, Multinomial Logistic Regression, and Bayesian Logistic Regression) on two datasets, our experiments show that query event-relatedness can be predicted with high accuracy using the proposed features.