Barbara Made the News: Mining the Behavior of Crowds for Time-Aware Learning to Rank

Flávio Martins, João Magalhães, Jamie Callan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)


In Twitter, and other microblogging services, the generation of new content by the crowd is often biased towards immediacy: what is happening now. Prompted by the propagation of commentary and information through multiple mediums, users on the Web interact with and produce new posts about newsworthy topics and give rise to trending topics. This paper proposes to leverage on the behavioral dynamics of users to estimate the most relevant time periods for a topic. Our hypothesis stems from the fact that when a real-world event occurs it usually has peak times on the Web: a higher volume of tweets, new visits and edits to related Wikipedia articles, and news published about the event.

In this paper, we propose a novel time-aware ranking model that leverages on multiple sources of crowd signals. Our approach builds on two major novelties. First, a unifying approach that given query q, mines and represents temporal evidence from multiple sources of crowd signals. This allows us to predict the temporal relevance of documents for query q. Second, a principled retrieval model that integrates temporal signals in a learning to rank framework, to rank results according to the predicted temporal relevance. Evaluation on the TREC 2013 and 2014 Microblog track datasets demonstrates that the proposed model achieves a relative improvement of 13.2% over lexical retrieval models and 6.2% over a learning to rank baseline.
Original languageEnglish
Title of host publicationProceedings of the Ninth ACM International Conference on Web Search and Data Mining, San Francisco, CA, USA, February 22-25, 2016
Number of pages10
ISBN (Electronic)978-1-4503-3716-8
Publication statusPublished - 2016
Event9th Annual ACM International Conference on Web Search and Data Mining (WSDM) - San Francisco, United States
Duration: 22 Feb 201625 Feb 2016


Conference9th Annual ACM International Conference on Web Search and Data Mining (WSDM)
CountryUnited States
CitySan Francisco


  • Microblog search
  • Twitter
  • social media
  • Learning to Rank
  • Time-aware ranking models
  • Temporal Information Retrieval

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