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Related Experiment Video

Updated: Mar 23, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Comparing writing style feature-based classification methods for estimating user reputations in social media.

Jong Hwan Suh1

  • 1Moon Soul Graduate School of Future Strategy, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141 Republic of Korea.

Springerplus
|March 24, 2016
PubMed
Summary

This study introduces an automatic approach using writing style features to detect manipulated user reputations in social media. The best accuracy was achieved using content-specific features and ensemble learning methods, particularly Random Subspace Support Vector Machine (RS-SVM).

Keywords:
Classification techniquesComparative studiesEnsemble learningSocial mediaUser reputation estimationWriting style features

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Area of Science:

  • Social Media Analysis
  • Computational Linguistics
  • Machine Learning

Background:

  • Internet anonymity complicates detecting manipulated user reputations and ensuring content quality.
  • Developing automated methods to assess user reputation is crucial for social media platforms.

Purpose of the Study:

  • To design and evaluate an automatic approach for estimating user reputations in social media using writing style features.
  • To compare the performance of various writing style features and machine learning classification techniques.

Main Methods:

  • Utilized four writing style features: lexical, syntactic, structural, and content-specific.
  • Evaluated eight classification techniques, including base learners (C4.5, Neural Network, Support Vector Machine, Naïve Bayes) and Random Subspace (RS) ensemble methods.
  • Tested the approach on South Korea's Daum Agora web forum, defining user reputations using like, dislike, sum, and portfolio methods.

Main Results:

  • The combination of content-specific features and the Random Subspace Support Vector Machine (RS-SVM) ensemble method yielded the highest classification accuracy.
  • Ensemble learning methods significantly outperformed base learners.
  • The portfolio approach for segmenting user reputations into 'Good' and 'Bad' classes provided the most accurate results.

Conclusions:

  • Writing style features, especially content-specific ones, are effective for estimating user reputations in social media.
  • Ensemble learning techniques enhance the accuracy of reputation classification.
  • The portfolio approach offers a robust method for defining reputation classes in social media analysis.