인스타그램 기반의 전이학습과 게시글 메타 정보를 활용한 페이스북 스팸 게시글 판별Facebook Spam Post Filtering based on Instagram-based Transfer Learning and Meta Information of Posts
- Other Titles
- Facebook Spam Post Filtering based on Instagram-based Transfer Learning and Meta Information of Posts
- Authors
- 김준홍; 서덕성; 김해동; 강필성
- Issue Date
- 2017
- Publisher
- 대한산업공학회
- Keywords
- Spam Filtering; Facebook; Instagram; Hash Tag; Random Forest; Transfer Learning
- Citation
- 대한산업공학회지, v.43, no.3, pp.192 - 202
- Indexed
- KCI
- Journal Title
- 대한산업공학회지
- Volume
- 43
- Number
- 3
- Start Page
- 192
- End Page
- 202
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/85935
- DOI
- 10.7232/JKIIE.2017.43.3.192
- ISSN
- 1225-0988
- 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.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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