Effectiveness of Usability Performance Features for Web Credibility Evaluation

Kenta Yamada, Hayato Yamana

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

Abstract

Unreliable web pages, such as fake news, have become an unavoidable problem. To tackle this problem, recent researches have adopted both content and social features to predict the credibility of the web pages; however, the accuracy is almost saturated. In this paper, we propose the adoption of Google Lighthouse features to predict web page credibility. Our experimental results show that the proposed method achieves an increased accuracy of 7.9% in comparison with state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6257-6259
Number of pages3
ISBN (Electronic)9781728108582
DOIs
Publication statusPublished - 2019 Dec
Event2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
Duration: 2019 Dec 92019 Dec 12

Publication series

NameProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference2019 IEEE International Conference on Big Data, Big Data 2019
CountryUnited States
CityLos Angeles
Period19/12/919/12/12

Keywords

  • fake sites
  • unreliable web pages
  • web credibility

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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  • Cite this

    Yamada, K., & Yamana, H. (2019). Effectiveness of Usability Performance Features for Web Credibility Evaluation. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 6257-6259). [9006419] (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006419