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[학술저널]

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김준홍(고려대학교) 서덕성(고려대학교) 김해동(고려대학교) 강필성(고려대학교)

DOI : 10.7232/JKIIE.2017.43.3.192

UCI(KEPA) : I410-ECN-0101-2018-530-000895276

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초록

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.

목차

1. 서론
2. 연구 프레임워크
3. 데이터 수집
4. 데이터 전처리
5. 스팸 분류 모델 구축
6. 결론
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