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A novel framework for social web forums’ thread ranking based on semantics and post quality features

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Abstract

Online discussion forums are a valuable source of knowledge. Users may share or exchange ideas by posting content in the form of questions and answers. With the increasing volume of online content in the form of forums, finding relevant information in forums can be a challenging task and knowledge management and quality assurance of this content are of critical importance. Although online discussion forums offer search services, in most cases only keyword search is provided. In keyword search techniques, such as cosine similarity, lexical overlap between query and document terms is considered; however, these techniques do not consider the context or meaning of the terms, thus failed to retrieve the relevant documents. Earlier content-based research efforts for improving the performance of thread retrieval were primarily based on cosine similarity technique. Cosine similarity technique assigns term-weights based on term-frequency and inverse-document frequency; however, this technique does not consider discussion semantics which may lead to less effective document retrieval. To address these issues, we have proposed two thread ranking techniques for online discussion forums: (1) threads are ranked on the basis of a semantic similarity score between posts and (2) threads are ranked based on their participants’ reputation and posts’ quality. The proposed work provides a performance comparison between semantic similarity techniques and cosine similarity techniques along with reputation and post quality features in thread ranking process. Experimental results obtained using a real online forum dataset demonstrate that the proposed techniques have significantly improved thread ranking performance.

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Notes

  1. https://wordnet.princeton.edu/.

  2. http://wordnet.princeton.edu.

  3. http://www.cyberemotions.eu/data.html.

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2061978).

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Authors and Affiliations

  1. Department of Computer Science, COMSATS Institute of IT, Attock, Pakistan

    Ch. Muhammad Shahzad Faisal & Faisal Imran

  2. Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan

    Ch. Muhammad Shahzad Faisal & Ali Daud

  3. Faculty of Computing and Information Technology, King Abdul Aziz University, Jeddah, Saudi Arabia

    Ali Daud

  4. Department of Media Software, Sungkyul University, Anyang, Korea

    Seungmin Rho

Authors
  1. Ch. Muhammad Shahzad Faisal
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  2. Ali Daud
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  3. Faisal Imran
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  4. Seungmin Rho
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Correspondence to Ch. Muhammad Shahzad Faisal or Seungmin Rho.

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Faisal, C.M.S., Daud, A., Imran, F. et al. A novel framework for social web forums’ thread ranking based on semantics and post quality features. J Supercomput 72, 4276–4295 (2016). https://doi.org/10.1007/s11227-016-1839-z

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