External Content-dependent Features for Web Credibility Evaluation

Kazuyoshi Ootani, Hayato Yamana

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Unreliable web pages such as fake news has become a global problem in big data era. The motivation to publish fake news is often for profit; for example, earning advertisement income by putting ads on their web pages. In this paper, we focus on different usage of HTML source tags between reliable and unreliable web pages, then propose new features for predicting their credibility. The experimental result shows that our proposed features increase accuracy when used together with previously proposed Contents features.

本文言語English
ホスト出版物のタイトルProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
編集者Yang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
出版社Institute of Electrical and Electronics Engineers Inc.
ページ5414-5416
ページ数3
ISBN(電子版)9781538650356
DOI
出版ステータスPublished - 2019 1 22
イベント2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
継続期間: 2018 12 102018 12 13

出版物シリーズ

名前Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference2018 IEEE International Conference on Big Data, Big Data 2018
CountryUnited States
CitySeattle
Period18/12/1018/12/13

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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