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The Impacts of Structural Difference and Temporality of Tweets on Retrieval Effectiveness

Authors:

Weiyi MengAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 31, Issue 4

Article No.: 21, Pages 1 - 38

https://doi.org/10.1145/2500751

Published: 01 November 2013 Publication History

Abstract

To explore the information seeking behaviors in microblogosphere, the microblog track at TREC 2011 introduced a real-time ad-hoc retrieval task that aims at ranking relevant tweets in reverse-chronological order. We study this problem via a two-phase approach: 1) retrieving tweets in an ad-hoc way; 2) utilizing the temporal information of tweets to enhance the retrieval effectiveness of tweets. Tweets can be categorized into two types. One type consists of short messages not containing any URL of a Web page. The other type has at least one URL of a Web page in addition to a short message. These two types of tweets have different structures. In the first phase, to address the structural difference of tweets, we propose a method to rank tweets using the divide-and-conquer strategy. Specifically, we first rank the two types of tweets separately. This produces two rankings, one for each type. Then we merge these two rankings of tweets into one ranking. In the second phase, we first categorize queries into several types by exploring the temporal distributions of their top-retrieved tweets from the first phase; then we calculate the time-related relevance scores of tweets according to the classified types of queries; finally we combine the time scores with the IR scores from the first phase to produce a ranking of tweets. Experimental results achieved by using the TREC 2011 and TREC 2012 queries over the TREC Tweets2011 collection show that: (i) our way of ranking the two types of tweets separately and then merging them together yields better retrieval effectiveness than ranking them simultaneously; (ii) our way of incorporating temporal information into the retrieval process yields further improvements, and (iii) our method compares favorably with state-of-the-art methods in retrieval effectiveness.

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Cited By

Hlaoua L(2024)An overview of aggregation methods for social networks analysisKnowledge and Information Systems10.1007/s10115-024-02296-zOnline publication date: 12-Dec-2024
https://doi.org/10.1007/s10115-024-02296-z
Ibtihel BLobna HLotfi B(2019)A Deep Learning-based Ranking Approach for Microblog RetrievalProcedia Computer Science10.1016/j.procs.2019.09.190159(352-362)Online publication date: 2019
https://doi.org/10.1016/j.procs.2019.09.190
CHY AULLAH MAONO M(2017)Microblog Retrieval Using Ensemble of Feature Sets through Supervised Feature SelectionIEICE Transactions on Information and Systems10.1587/transinf.2016DAP0032E100.D:4(793-806)Online publication date: 2017
https://doi.org/10.1587/transinf.2016DAP0032
Show More Cited By

Index Terms

The Impacts of Structural Difference and Temporality of Tweets on Retrieval Effectiveness
1. Information systems
  1. Information retrieval
    1. Document representation
  2. Information systems applications

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Reviews

Reviewer: Xiannong Meng

A novel method of evaluating tweets is introduced in this paper. Most retrieval algorithms do not differentiate the structure of tweets. The authors show convincingly that it does have an impact on retrieval effectiveness. In their study, two types of tweets are evaluated separately, those containing only plain text, and those containing any URLs. The key is that in ranking the tweets with URLs, the ranker considers the content of the page(s) pointed to by the URLs in addition to the tweets. After ranking the two types of tweets, a support vector machine-based classifier with 18 features is used to evaluate the relevance between the tweets and the query. If a tweet is time-sensitive, the temporal information of both the tweet and its parent is taken into consideration. Data from TREC 2011, TREC 2012, and TREC Tweets 2011 are used to evaluate the algorithm. The results indicate that (1) it is more effective to rank the two types of tweets separately and then merge them; (2) incorporating temporal information yields further improvements; and (3) the proposed "method compares favorably with state-of-the-art methods in retrieval effectiveness." The novelty of the proposed method is that it offers the ability to rank the tweets with and without URLs separately and to incorporate the temporal information in ranking the tweets. The paper is well written and self-contained. Many examples illustrate the concepts discussed. The readers can explore the topic further using the abundant references provided. Online Computing Reviews Service

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 31, Issue 4

November 2013

192 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/2536736

Editor:
Jamie Callan
Carnegie Mellon University, USA

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Copyright © 2013 ACM.

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Association for Computing Machinery

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Publication History

Published: 01 November 2013

Accepted: 01 July 2013

Revised: 01 April 2013

Received: 01 September 2012

Published in TOIS Volume 31, Issue 4

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Cited By

Hlaoua L(2024)An overview of aggregation methods for social networks analysisKnowledge and Information Systems10.1007/s10115-024-02296-zOnline publication date: 12-Dec-2024
https://doi.org/10.1007/s10115-024-02296-z
Ibtihel BLobna HLotfi B(2019)A Deep Learning-based Ranking Approach for Microblog RetrievalProcedia Computer Science10.1016/j.procs.2019.09.190159(352-362)Online publication date: 2019
https://doi.org/10.1016/j.procs.2019.09.190
CHY AULLAH MAONO M(2017)Microblog Retrieval Using Ensemble of Feature Sets through Supervised Feature SelectionIEICE Transactions on Information and Systems10.1587/transinf.2016DAP0032E100.D:4(793-806)Online publication date: 2017
https://doi.org/10.1587/transinf.2016DAP0032
Chy AUllah MAono M(2015)Combining temporal and content aware features for microblog retrieval2015 2nd International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA)10.1109/ICAICTA.2015.7335353(1-6)Online publication date: Aug-2015
https://doi.org/10.1109/ICAICTA.2015.7335353

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