Fake News Detection System using Article Abstraction

Kyeong Hwan Kim, Chang Sung Jeong

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

Abstract

Recently, fake news has been incurring many problems to our society. As a result, many researchers have been working on identifying fake news. Most of the fake news detection systems utilize the linguistic feature of the news. However, they have difficulty in sensing highly ambiguous fake news which can be detected only after identifying meaning and latest related information. In this paper, to resolve this problem, we shall present a new Korean fake news detection system using fact DB which is built and updated by human's direct judgement after collecting obvious facts. Our system receives a proposition, and search the semantically related articles from Fact DB in order to verify whether the given proposition is true or not by comparing the proposition with the related articles in fact DB. To achieve this, we utilize a deep learning model, Bidirectional Multi-Perspective Matching for Natural Language Sentence(BiMPM), which has demonstrated a good performance for the sentence matching task. However, BiMPM has some limitations in that the longer the length of the input sentence is, the lower its performance is, and it has difficulty in making an accurate judgement when an unlearned word or relation between words appear. In order to overcome the limitations, we shall propose a new matching technique which exploits article abstraction as well as entity matching set in addition to BiMPM. In our experiment, we shall show that our system improves the whole performance for fake news detection.

Original languageEnglish
Title of host publicationJCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering
Subtitle of host publicationKnowledge Evolution Towards Singularity of Man-Machine Intelligence
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages209-212
Number of pages4
ISBN (Electronic)9781728107196
DOIs
Publication statusPublished - 2019 Jul
Event16th International Joint Conference on Computer Science and Software Engineering, JCSSE 2019 - Chonburi, Thailand
Duration: 2019 Jul 102019 Jul 12

Publication series

NameJCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering: Knowledge Evolution Towards Singularity of Man-Machine Intelligence

Conference

Conference16th International Joint Conference on Computer Science and Software Engineering, JCSSE 2019
CountryThailand
CityChonburi
Period19/7/1019/7/12

Keywords

  • BiLSTM model
  • Deep learning
  • Fake news detection
  • Natural Language Processing
  • Sentence matching

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Information Systems and Management

Fingerprint Dive into the research topics of 'Fake News Detection System using Article Abstraction'. Together they form a unique fingerprint.

  • Cite this

    Kim, K. H., & Jeong, C. S. (2019). Fake News Detection System using Article Abstraction. In JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering: Knowledge Evolution Towards Singularity of Man-Machine Intelligence (pp. 209-212). [8864154] (JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering: Knowledge Evolution Towards Singularity of Man-Machine Intelligence). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/JCSSE.2019.8864154