IEICE TRANSACTIONS on Information
A Survey on Explainable Fake News Detection
Summary :
The increasing amount of fake news is a growing problem that will progressively worsen in our interconnected world. Machine learning, particularly deep learning, is being used to detect misinformation; however, the models employed are essentially black boxes, and thus are uninterpretable. This paper presents an overview of explainable fake news detection models. Specifically, we first review the existing models, datasets, evaluation techniques, and visualization processes. Subsequently, possible improvements in this field are identified and discussed.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.7 pp.1249-1257
- Publication Date
- 2022/07/01
- Publicized
- 2022/04/22
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021EDR0003
- Type of Manuscript
- SURVEY PAPER
- Category
- Data Engineering, Web Information Systems
Authors
Ken MISHIMA
Waseda University
Hayato YAMANA
Waseda University
Keyword
Latest Issue
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Ken MISHIMA, Hayato YAMANA, "A Survey on Explainable Fake News Detection" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 7, pp. 1249-1257, July 2022, doi: 10.1587/transinf.2021EDR0003.
Abstract: The increasing amount of fake news is a growing problem that will progressively worsen in our interconnected world. Machine learning, particularly deep learning, is being used to detect misinformation; however, the models employed are essentially black boxes, and thus are uninterpretable. This paper presents an overview of explainable fake news detection models. Specifically, we first review the existing models, datasets, evaluation techniques, and visualization processes. Subsequently, possible improvements in this field are identified and discussed.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDR0003/_p
Copy
@ARTICLE{e105-d_7_1249,
author={Ken MISHIMA, Hayato YAMANA, },
journal={IEICE TRANSACTIONS on Information},
title={A Survey on Explainable Fake News Detection},
year={2022},
volume={E105-D},
number={7},
pages={1249-1257},
abstract={The increasing amount of fake news is a growing problem that will progressively worsen in our interconnected world. Machine learning, particularly deep learning, is being used to detect misinformation; however, the models employed are essentially black boxes, and thus are uninterpretable. This paper presents an overview of explainable fake news detection models. Specifically, we first review the existing models, datasets, evaluation techniques, and visualization processes. Subsequently, possible improvements in this field are identified and discussed.},
keywords={},
doi={10.1587/transinf.2021EDR0003},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - A Survey on Explainable Fake News Detection
T2 - IEICE TRANSACTIONS on Information
SP - 1249
EP - 1257
AU - Ken MISHIMA
AU - Hayato YAMANA
PY - 2022
DO - 10.1587/transinf.2021EDR0003
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2022
AB - The increasing amount of fake news is a growing problem that will progressively worsen in our interconnected world. Machine learning, particularly deep learning, is being used to detect misinformation; however, the models employed are essentially black boxes, and thus are uninterpretable. This paper presents an overview of explainable fake news detection models. Specifically, we first review the existing models, datasets, evaluation techniques, and visualization processes. Subsequently, possible improvements in this field are identified and discussed.
ER -
English
