Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Facebook Spam Post Filtering based on Instagram-based Transfer Learning and Meta Information of Posts

인스타그램 기반의 전이학습과 게시글 메타 정보를 활용한 페이스북 스팸 게시글 판별

  • Kim, Junhong (School of Industrial Management Engineering, Korea University) ;
  • Seo, Deokseong (School of Industrial Management Engineering, Korea University) ;
  • Kim, Haedong (School of Industrial Management Engineering, Korea University) ;
  • Kang, Pilsung (School of Industrial Management Engineering, Korea University)
  • 김준홍 (고려대학교 산업경영공학부) ;
  • 서덕성 (고려대학교 산업경영공학부) ;
  • 김해동 (고려대학교 산업경영공학부) ;
  • 강필성 (고려대학교 산업경영공학부)
  • Received : 2016.08.13
  • Accepted : 2017.02.18
  • Published : 2017.06.15
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Abstract

This study develops a text spam filtering system for Facebook based on two variable categories: keywords learned from Instagram and meta-information of Facebook posts. Since there is no explicit labels for spam/ham posts, we utilize hash tags in Instagram to train classification models. In addition, the filtering accuracy is enhanced by considering meta-information of Facebook posts. To verify the proposed filtering system, we conduct an empirical experiment based on a total of 1,795,067 and 761,861 Facebook and Instagram documents, respectively. Employing random forest as a base classification algorithm, experimental result shows that the proposed filtering system yield 99% and 98% in terms of filtering accuracy and F1-measure, respectively. We expect that the proposed filtering scheme can be applied other web services suffering from massive spam posts but no explicit spam labels are available.

Keywords

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