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A Generalized Framework for Quantifying Trust of Social Media Text Documents

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9729))

Abstract

Social media has become a very popular place for users seeking knowledge about a wide variety of topics. While it contains many helpful documents, it also contains many useless and malicious documents or spams. For a casual observer it is very hard to identify high quality or trustworthy documents. As the volume of such data increases, the task for identifying the trustworthy documents becomes more and more difficult. A huge number of research works have focused on quantifying trust in certain specific social network domains. Some have quantified trust based on social graph. In this work, we use such social graph named Reduced node Social Graph with Relationships (RSGR) and we develop a three-step syntax and semantic based trust mining framework. Here we generalize the concept of trust mining for all structured as well as unstructured unsupervised text documents from all social network domains. We calculate trust based on metadata, trust based on relationships with other documents and finally we propagate the trust calculated so far along various relationship edges to calculate the final trust. Finally we show that our method calculates the trust of social media text documents with more than 80% accuracy.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India

    Tuhin Sharma & Durga Toshniwal

Authors
  1. Tuhin Sharma
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  2. Durga Toshniwal
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Corresponding author

Correspondence to Tuhin Sharma .

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Editors and Affiliations

  1. IBaI, Inst of Comp Vision and applied Comp Sci, Leipzig, Sachsen, Germany

    Petra Perner

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Sharma, T., Toshniwal, D. (2016). A Generalized Framework for Quantifying Trust of Social Media Text Documents. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_53

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  • DOI: https://doi.org/10.1007/978-3-319-41920-6_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41919-0

  • Online ISBN: 978-3-319-41920-6

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