External Content-dependent Features for Web Credibility Evaluation

Kazuyoshi Ootani, Hayato Yamana

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang 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
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5414-5416
Number of pages3
ISBN (Electronic)9781538650356
DOIs
Publication statusPublished - 2019 Jan 22
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: 2018 Dec 102018 Dec 13

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

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

Keywords

  • fake sites;
  • unreliable web pages
  • web credibility

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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