FaNDeR: Fake News Detection Model Using Media Reliability

Youngkyung Seo; Deokjin Seo; Chang Sung Jeong

doi:10.1109/TENCON.2018.8650350

FaNDeR: Fake News Detection Model Using Media Reliability

Youngkyung Seo, Deokjin Seo, Chang Sung Jeong

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1834-1838
Number of pages	5
ISBN (Electronic)	9781538654576
DOIs	https://doi.org/10.1109/TENCON.2018.8650350
Publication status	Published - 2019 Feb 22
Event	2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of Duration: 2018 Oct 28 → 2018 Oct 31

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume	2018-October
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Conference

Conference	2018 IEEE Region 10 Conference, TENCON 2018
Country/Territory	Korea, Republic of
City	Jeju
Period	18/10/28 → 18/10/31

Keywords

Deep learning
Fake news
Media
Question Answering System;
Reliability
Source

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TENCON.2018.8650350

Cite this

Seo, Y., Seo, D., & Jeong, C. S. (2019). FaNDeR: Fake News Detection Model Using Media Reliability. In Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference (pp. 1834-1838). [8650350] (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2018-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2018.8650350

FaNDeR : Fake News Detection Model Using Media Reliability. / Seo, Youngkyung; Seo, Deokjin; Jeong, Chang Sung.

Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1834-1838 8650350 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Seo, Y, Seo, D & Jeong, CS 2019, FaNDeR: Fake News Detection Model Using Media Reliability. in Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference., 8650350, IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2018-October, Institute of Electrical and Electronics Engineers Inc., pp. 1834-1838, 2018 IEEE Region 10 Conference, TENCON 2018, Jeju, Korea, Republic of, 18/10/28. https://doi.org/10.1109/TENCON.2018.8650350

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title = "FaNDeR: Fake News Detection Model Using Media Reliability",

abstract = "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.",

keywords = "Deep learning, Fake news, Media, Question Answering System;, Reliability, Source",

author = "Youngkyung Seo and Deokjin Seo and Jeong, {Chang Sung}",

note = "Funding Information: ACKNOWLEDGMENT This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03035461), the Brain Korea 21 Plus Project in 2018, and the Institute for Information & communications Technology Promotion(IITP) grant funded by the Korean government (MSIP) (No. 2018-0-00739, Deep learning-based natural language contents evaluation technology for detecting fake news). Funding Information: This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03035461) Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Region 10 Conference, TENCON 2018 ; Conference date: 28-10-2018 Through 31-10-2018",

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N2 - 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.

AB - 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.

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FaNDeR: Fake News Detection Model Using Media Reliability

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Keywords

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