FAMOUS: Fake News Detection Model Based on Unified Key Sentence Information

Namwon Kim; Deokjin Seo; Chang Sung Jeong

doi:10.1109/ICSESS.2018.8663864

FAMOUS: Fake News Detection Model Based on Unified Key Sentence Information

Namwon Kim, Deokjin Seo, Chang Sung Jeong

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

8 Citations (Scopus)

Abstract

Fake news detection causes a challenging problem due to the great influence of communication media over the public. In this paper, we shall present a new fake news detection model using unified key sentence information which can efficiently perform sentence matching between question and article by using key sentence retrieval based on bilateral multi perspective matching model. Our model makes use of one unified word vector for the key sentences of article by extracting them to the question from article and then merging the word vector for each key sentence. It can efficiently perform the sentence matching by executing matching operations between the contextual information obtained from the word vectors of question and key sentences through bidirectional long short term memory. Our model shows the competitive performance for fake news detection on the Korean article dataset over the previous result.

Original language	English
Title of host publication	ICSESS 2018 - Proceedings of 2018 IEEE 9th International Conference on Software Engineering and Service Science
Editors	Li Wenzheng, M. Surendra Prasad Babu
Publisher	IEEE Computer Society
Pages	617-620
Number of pages	4
ISBN (Electronic)	9781538665640
DOIs	https://doi.org/10.1109/ICSESS.2018.8663864
Publication status	Published - 2018 Jul 2
Event	9th IEEE International Conference on Software Engineering and Service Science, ICSESS 2018 - Beijing, China Duration: 2018 Nov 23 → 2018 Nov 25

Publication series

Name	Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS
Volume	2018-November
ISSN (Print)	2327-0586
ISSN (Electronic)	2327-0594

Conference

Conference	9th IEEE International Conference on Software Engineering and Service Science, ICSESS 2018
Country/Territory	China
City	Beijing
Period	18/11/23 → 18/11/25

Bibliographical note

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 (2017R1D1AlB03035461), the Brain Korea 21 Plus Project in 2018, and the Institute for Information & Communications Technology Promotion(IITP) grant funded by the Korea government (MSIT) (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 (2017R1D1AlB03035461).

Publisher Copyright:
© 2018 IEEE.

Keywords

fake news detectiont
key sentence retrieval
natural language processing
sentence matching

ASJC Scopus subject areas

Software

Access to Document

10.1109/ICSESS.2018.8663864

Cite this

Kim, N., Seo, D., & Jeong, C. S. (2018). FAMOUS: Fake News Detection Model Based on Unified Key Sentence Information. In L. Wenzheng, & M. S. P. Babu (Eds.), ICSESS 2018 - Proceedings of 2018 IEEE 9th International Conference on Software Engineering and Service Science (pp. 617-620). Article 8663864 (Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS; Vol. 2018-November). IEEE Computer Society. https://doi.org/10.1109/ICSESS.2018.8663864

FAMOUS: Fake News Detection Model Based on Unified Key Sentence Information. / Kim, Namwon; Seo, Deokjin; Jeong, Chang Sung.
ICSESS 2018 - Proceedings of 2018 IEEE 9th International Conference on Software Engineering and Service Science. ed. / Li Wenzheng; M. Surendra Prasad Babu. IEEE Computer Society, 2018. p. 617-620 8663864 (Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS; Vol. 2018-November).

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

Kim, N, Seo, D & Jeong, CS 2018, FAMOUS: Fake News Detection Model Based on Unified Key Sentence Information. in L Wenzheng & MSP Babu (eds), ICSESS 2018 - Proceedings of 2018 IEEE 9th International Conference on Software Engineering and Service Science., 8663864, Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, vol. 2018-November, IEEE Computer Society, pp. 617-620, 9th IEEE International Conference on Software Engineering and Service Science, ICSESS 2018, Beijing, China, 18/11/23. https://doi.org/10.1109/ICSESS.2018.8663864

Kim N, Seo D, Jeong CS. FAMOUS: Fake News Detection Model Based on Unified Key Sentence Information. In Wenzheng L, Babu MSP, editors, ICSESS 2018 - Proceedings of 2018 IEEE 9th International Conference on Software Engineering and Service Science. IEEE Computer Society. 2018. p. 617-620. 8663864. (Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS). doi: 10.1109/ICSESS.2018.8663864

Kim, Namwon ; Seo, Deokjin ; Jeong, Chang Sung. / FAMOUS : Fake News Detection Model Based on Unified Key Sentence Information. ICSESS 2018 - Proceedings of 2018 IEEE 9th International Conference on Software Engineering and Service Science. editor / Li Wenzheng ; M. Surendra Prasad Babu. IEEE Computer Society, 2018. pp. 617-620 (Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS).

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abstract = "Fake news detection causes a challenging problem due to the great influence of communication media over the public. In this paper, we shall present a new fake news detection model using unified key sentence information which can efficiently perform sentence matching between question and article by using key sentence retrieval based on bilateral multi perspective matching model. Our model makes use of one unified word vector for the key sentences of article by extracting them to the question from article and then merging the word vector for each key sentence. It can efficiently perform the sentence matching by executing matching operations between the contextual information obtained from the word vectors of question and key sentences through bidirectional long short term memory. Our model shows the competitive performance for fake news detection on the Korean article dataset over the previous result.",

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note = "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 (2017R1D1AlB03035461), the Brain Korea 21 Plus Project in 2018, and the Institute for Information & Communications Technology Promotion(IITP) grant funded by the Korea government (MSIT) (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 (2017R1D1AlB03035461). Publisher Copyright: {\textcopyright} 2018 IEEE.; 9th IEEE International Conference on Software Engineering and Service Science, ICSESS 2018 ; Conference date: 23-11-2018 Through 25-11-2018",

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FAMOUS: Fake News Detection Model Based on Unified Key Sentence Information

Abstract

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Bibliographical note

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ASJC Scopus subject areas

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