FaNDeR: Fake News Detection Model Using Media Reliability

Youngkyung Seo, Deokjin Seo, Chang Sung Jeong

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

8 Citations (Scopus)


With the development of media including newspaper written by robots and many unreliable sources, it's getting hard to distinguish whether the news is true or not. In this paper, we shall present a novel fake news detection model, FaNDeR(Fake News Detection model using media Reliability) which can efficiently classify the level of truth for the news in the question answering system based on modified CNN deep learning model. Our model reflects the reliability of various medias by training with the input dataset which contains the truthfulness of each media as well as that of the proposition. Our model is designed for higher accuracy with media dataset in terms of data augmentation, batch size control and model modification. We shall show that our model has higher accuracy over statistical approach by reflecting the tendency of truth level for each media through the training of the dataset collected so far.

Original languageEnglish
Title of host publicationProceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781538654576
Publication statusPublished - 2019 Feb 22
Event2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of
Duration: 2018 Oct 282018 Oct 31

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2018 IEEE Region 10 Conference, TENCON 2018
Country/TerritoryKorea, Republic of


  • Deep learning
  • Fake news
  • Media
  • Question Answering System;
  • Reliability
  • Source

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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