Temporal models for microblogs

Time information impacts relevance in retrieval for the queries that are sensitive to trends and events. Microblog services particularly focused on recent news and events so dealing with the temporal aspects of microblogs is essential for providing effective retrieval. Recent work on time-based retrieval has shown that selecting the relevant time period for query expansion is promising. In this paper, we suggest a method for selecting the time period for query expansion based on a user behavior (i.e., retweets) that can be collected easily. We then use these time periods for query expansion in a pseudo-relevance feedback setting. More specifically, we use the difference in the temporal distribution between the top retrieved documents and retweets. The experimental results based on the TREC Microblog collection show that our method for selecting periods for query expansion improves retrieval performance compared to another approach.

[1]  Karen Rose,et al.  What is Twitter , 2009 .

[2]  James Allan,et al.  A comparison of statistical significance tests for information retrieval evaluation , 2007, CIKM '07.

[3]  Meredith Ringel Morris,et al.  #TwitterSearch: a comparison of microblog search and web search , 2011, WSDM '11.

[4]  W. Bruce Croft,et al.  Relevance-Based Language Models , 2001, SIGIR '01.

[5]  M. de Rijke,et al.  Incorporating Query Expansion and Quality Indicators in Searching Microblog Posts , 2011, ECIR.

[6]  Luis Gravano,et al.  Answering General Time-Sensitive Queries , 2012, IEEE Trans. Knowl. Data Eng..

[7]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[8]  Rizal Setya Perdana.  What is Twitter , 2013 .

[9]  Mostafa Keikha,et al.  Time-based relevance models , 2011, SIGIR.

[10]  Brian D. Davison,et al.  Predicting popular messages in Twitter , 2011, WWW.

[11]  Thomas Gottron,et al.  Searching microblogs: coping with sparsity and document quality , 2011, CIKM '11.

[12]  Eduard H. Hovy,et al.  Structured Event Retrieval over Microblog Archives , 2012, NAACL.

[13]  Ying Zhang,et al.  Retweet Modeling Using Conditional Random Fields , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[14]  W. Bruce Croft,et al.  Time-based language models , 2003, CIKM '03.

[15]  Fernando Diaz,et al.  Temporal profiles of queries , 2007, TOIS.

[16]  Fernando Diaz,et al.  Using temporal profiles of queries for precision prediction , 2004, SIGIR '04.

[17]  Miles Efron,et al.  Estimation methods for ranking recent information , 2011, SIGIR.

[18]  W. Bruce Croft,et al.  A Markov random field model for term dependencies , 2005, SIGIR '05.

[19]  M. de Rijke,et al.  Adaptive Temporal Query Modeling , 2012, ECIR.