Predicting the News of Tomorrow Using Patterns inWeb Search Queries

Kira Radinsky, Sagie Davidovich and Shaul Markovitch. Predicting the News of Tomorrow Using Patterns inWeb Search Queries. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence (WI'08), 363-367 Sydney, Australia, 2008.

Abstract

The novel task we aim at in this work is to predict top terms that will prominently appear in the future news. This is a difficult task that nobody attempted before, as far as we know. We present a novel methodology for using patterns of user queries to predict future events. Query history is obtained from web resources such as Google Trends. In order to predict whether a term will appear in tomorrow's news, we examine if the terms in today's queries indicated this term in the past. We provide empirical support for the effectiveness of our method by showing its prediction power on news archives.

Keywords: Information Retrieval, Temporal Reasoning, Prediction

Secondary Keywords:

Online version:

Bibtex entry:

 @inproceedings{Radinsky:2008:PNT,
  Author = {Kira Radinsky and Sagie Davidovich and Shaul Markovitch},
  Title = {Predicting the News of Tomorrow Using Patterns inWeb Search Queries},
  Year = {2008},
  Booktitle = {Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence (WI'08)},
  Pages = {363--367},
  Address = {Sydney, Australia},
  Url = {http://www.cs.technion.ac.il/~shaulm/papers/pdf/Radinsky-WI2008.pdf},
  Keywords = {Information Retrieval, Temporal Reasoning, Prediction},
  Secondary-keywords = {Common-Sense Knowledge},
  Abstract = {
    The novel task we aim at in this work is to predict top terms that
    will prominently appear in the future news. This is a difficult
    task that nobody attempted before, as far as we know. We present a
    novel methodology for using patterns of user queries to predict
    future events. Query history is obtained from web resources such
    as Google Trends. In order to predict whether a term will appear
    in tomorrow's news, we examine if the terms in today's queries
    indicated this term in the past. We provide empirical support for
    the effectiveness of our method by showing its prediction power on
    news archives.
  }

  }