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Title of Journal: J Supercomput

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Abbravation: The Journal of Supercomputing

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Springer US

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10.1007/bf03278522

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1573-0484

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A novel framework for social web forums’ thread ra

Authors: Ch Muhammad Shahzad Faisal Ali Daud Faisal Imran Seungmin Rho
Publish Date: 2016/08/23
Volume: 72, Issue: 11, Pages: 4276-4295
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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 contentbased research efforts for improving the performance of thread retrieval were primarily based on cosine similarity technique Cosine similarity technique assigns termweights based on termfrequency and inversedocument 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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