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Automatic prediction of news intent for search queries: An exploration of contextual and temporal features

Xiaojuan Zhang (Department of Computer and Information Science, Southwest University, Chongqing, China)
Shuguang Han (Department of Information Science, University of Pittsburgh, Pittsburgh, PA, USA)
Wei Lu (Department of Information Management, Wuhan University, Hubei, China)

The Electronic Library

ISSN: 0264-0473

Article publication date: 5 November 2018

Issue publication date: 5 November 2018

210

Abstract

Purpose

The purpose of this paper is to predict news intent by exploring contextual and temporal features directly mined from a general search engine query log.

Design/methodology/approach

First, a ground-truth data set with correctly marked news and non-news queries was built. Second, a detailed analysis of the search goals and topics distribution of news/non-news queries was conducted. Third, three news features, that is, the relationship between entity and contextual words extended from query sessions, topical similarity among clicked results and temporal burst point were obtained. Finally, to understand the utilities of the new features and prior features, extensive prediction experiments on SogouQ (a Chinese search engine query log) were conducted.

Findings

News intent can be predicted with high accuracy by using the proposed contextual and temporal features, and the macro average F1 of classification is around 0.8677. Contextual features are more effective than temporal features. All the three new features are useful and significant in improving the accuracy of news intent prediction.

Originality/value

This paper provides a new and different perspective in recognizing queries with news intent without use of such large corpora as social media (e.g. Wikipedia, Twitter and blogs) and news data sets. The research will be helpful for general-purpose search engines to address search intents for news events. In addition, the authors believe that the approaches described here in this paper are general enough to apply to other verticals with dynamic content and interest, such as blog or financial data.

Keywords

Acknowledgements

This research is supported by National Social Science Foundation of China under grant no. 15CTQ019 and National Nature Science Foundation of China under grant no. 71173164.

Citation

Zhang, X., Han, S. and Lu, W. (2018), "Automatic prediction of news intent for search queries: An exploration of contextual and temporal features", The Electronic Library, Vol. 36 No. 5, pp. 938-958. https://doi.org/10.1108/EL-06-2017-0134

Publisher

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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