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Query-based unsupervised learning for improving social media search

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Abstract

In the current information era over the internet, social media has become one of the essential information sources for users. While the text is the primary information representation, finding relevant information is a challenging mission for researchers due to its nature (e.g., short length, sparseness). Acquiring high-quality search results from massive data, such as social media needs a set of representative query terms that are not always available. In this paper, we propose a novel query-based unsupervised learning model to represent the implicit relationships in the short text from social media. This bridges the gap of the lack of word co-occurrences without requiring many parameters to be estimated and external evidence to be collected. To confirm the proposed model effectiveness, we compare the proposed model with state-of-the-art lexical, topic model and temporal models on the large-scale TREC microblog 2011-2014 collections. The experimental results show that the proposed model significantly improved overall state-of-the-art lexical, topic model and temporal models with the maximum percentage of increase reaching 33.97% based on MAP value and 21.38% based on Precision at top 30 documents. The proposed model can improve the social media search effectiveness in potential closely retrieval tasks, such as question answering and timeline summarisation.

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Notes

  1. The proposed model called query-based unsupervised short text mining (QUSTM)

  2. http://github.com/lintool/twitter-tools

  3. http://github.com/shuyo/ldig

  4. The experiments are performed in a PC with an Intel(R) Core(TM) i7-4790 CPU @ 3.60 GHz and 16 GB memory running a Windows 7 operating system.

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Acknowledgements

This paper was partially supported by Grant DP140103157 from the Australian Research Council (ARC).

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Authors and Affiliations

  1. School of EECS, Queensland University of Technology (QUT), Brisbane, Australia

    Khaled Albishre, Yuefeng Li & Yue Xu

  2. Umm Al-Qura University, Makkah, Saudi Arabia

    Khaled Albishre

  3. School of Economy and Management, Hubei University of Technology, Wuhan, 430064, Hubei, China

    Wei Huang

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  1. Khaled Albishre
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  2. Yuefeng Li
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  4. Wei Huang
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Correspondence to Khaled Albishre.

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This article belongs to the Topical Collection: Computational Social Science as the Ultimate Web Intelligence

Guest Editors: Xiaohui Tao, Juan D. Velasquez, Jiming Liu, and Ning Zhong

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Albishre, K., Li, Y., Xu, Y. et al. Query-based unsupervised learning for improving social media search. World Wide Web 23, 1791–1809 (2020). https://doi.org/10.1007/s11280-019-00747-0

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