EmoKnow: emotion- and knowledge-oriented model for COVID-19 fake news detection

Yuchen Zhang; Xing Su; Jia Wu; Jian Yang; Hao Fan; Xiaochuan Zheng

doi:10.1007/978-3-031-46661-8_24

EmoKnow: emotion- and knowledge-oriented model for COVID-19 fake news detection

Yuchen Zhang, Xing Su, Jia Wu, Jian Yang, Hao Fan^*, Xiaochuan Zheng

^*Corresponding author for this work

School of Computing

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

1 Citation (Scopus)

Abstract

Content-based methods are inadequate for detecting fake news related to COVID-19 due to the complexity of this domain. Some studies integrate the social context information of the news to improve performance. However, such information is not consistently available and sometimes not helpful regarding COVID-19, as most users lack professional knowledge about it and may be unable to respond accurately. Additionally, fake news often employs emotional manipulation to exploit people’s emotions to shape their beliefs and actions. Therefore, we propose EmoKnow, an emotion- and knowledge-oriented model, for detecting fake news about COVID-19. Our proposed method incorporates language modeling, emotion feature extraction, and external knowledge sources to provide an informative representation of news. Experimental results on four COVID-19-related datasets show that EmoKnow significantly outperforms state-of-the-art approaches.

Original language	English
Title of host publication	Advanced Data Mining and Applications
Subtitle of host publication	19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, proceedings, part I
Editors	Xiaochun Yang, Heru Suhartanto, Guoren Wang, Bin Wang, Jing Jiang, Bing Li, Huaijie Zhu, Ningning Cui
Place of Publication	Cham
Publisher	Springer, Springer Nature
Pages	352-367
Number of pages	16
ISBN (Electronic)	9783031466618
ISBN (Print)	9783031466601
DOIs	https://doi.org/10.1007/978-3-031-46661-8_24
Publication status	Published - 2023
Event	19th International Conference on Advanced Data Mining and Applications, ADMA 2023 - Shenyang, China Duration: 21 Aug 2023 → 23 Aug 2023

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	14176
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Advanced Data Mining and Applications, ADMA 2023
Country/Territory	China
City	Shenyang
Period	21/08/23 → 23/08/23

Keywords

COVID-19
Fake News Detection
Text Embedding
Emotion Features
Knowledge Representation

Access to Document

10.1007/978-3-031-46661-8_24

Cite this

Zhang, Y., Su, X., Wu, J., Yang, J., Fan, H., & Zheng, X. (2023). EmoKnow: emotion- and knowledge-oriented model for COVID-19 fake news detection. In X. Yang, H. Suhartanto, G. Wang, B. Wang, J. Jiang, B. Li, H. Zhu, & N. Cui (Eds.), Advanced Data Mining and Applications: 19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, proceedings, part I (pp. 352-367). (Lecture Notes in Computer Science; Vol. 14176). Springer, Springer Nature. https://doi.org/10.1007/978-3-031-46661-8_24

Zhang, Yuchen ; Su, Xing ; Wu, Jia et al. / EmoKnow : emotion- and knowledge-oriented model for COVID-19 fake news detection. Advanced Data Mining and Applications: 19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, proceedings, part I. editor / Xiaochun Yang ; Heru Suhartanto ; Guoren Wang ; Bin Wang ; Jing Jiang ; Bing Li ; Huaijie Zhu ; Ningning Cui. Cham : Springer, Springer Nature, 2023. pp. 352-367 (Lecture Notes in Computer Science).

@inproceedings{520a2103d37d4a1daf017b235b1153c9,

title = "EmoKnow: emotion- and knowledge-oriented model for COVID-19 fake news detection",

abstract = "Content-based methods are inadequate for detecting fake news related to COVID-19 due to the complexity of this domain. Some studies integrate the social context information of the news to improve performance. However, such information is not consistently available and sometimes not helpful regarding COVID-19, as most users lack professional knowledge about it and may be unable to respond accurately. Additionally, fake news often employs emotional manipulation to exploit people{\textquoteright}s emotions to shape their beliefs and actions. Therefore, we propose EmoKnow, an emotion- and knowledge-oriented model, for detecting fake news about COVID-19. Our proposed method incorporates language modeling, emotion feature extraction, and external knowledge sources to provide an informative representation of news. Experimental results on four COVID-19-related datasets show that EmoKnow significantly outperforms state-of-the-art approaches.",

keywords = "COVID-19, Fake News Detection, Text Embedding, Emotion Features, Knowledge Representation",

author = "Yuchen Zhang and Xing Su and Jia Wu and Jian Yang and Hao Fan and Xiaochuan Zheng",

year = "2023",

doi = "10.1007/978-3-031-46661-8_24",

language = "English",

isbn = "9783031466601",

series = "Lecture Notes in Computer Science",

publisher = "Springer, Springer Nature",

pages = "352--367",

editor = "Xiaochun Yang and Heru Suhartanto and Guoren Wang and Bin Wang and Jing Jiang and Bing Li and Huaijie Zhu and Ningning Cui",

booktitle = "Advanced Data Mining and Applications",

address = "United States",

note = "19th International Conference on Advanced Data Mining and Applications, ADMA 2023 ; Conference date: 21-08-2023 Through 23-08-2023",

}

Zhang, Y, Su, X, Wu, J , Yang, J, Fan, H & Zheng, X 2023, EmoKnow: emotion- and knowledge-oriented model for COVID-19 fake news detection. in X Yang, H Suhartanto, G Wang, B Wang, J Jiang, B Li, H Zhu & N Cui (eds), Advanced Data Mining and Applications: 19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, proceedings, part I. Lecture Notes in Computer Science, vol. 14176, Springer, Springer Nature, Cham, pp. 352-367, 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, Shenyang, China, 21/08/23. https://doi.org/10.1007/978-3-031-46661-8_24

EmoKnow: emotion- and knowledge-oriented model for COVID-19 fake news detection. / Zhang, Yuchen; Su, Xing; Wu, Jia et al.
Advanced Data Mining and Applications: 19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, proceedings, part I. ed. / Xiaochun Yang; Heru Suhartanto; Guoren Wang; Bin Wang; Jing Jiang; Bing Li; Huaijie Zhu; Ningning Cui. Cham: Springer, Springer Nature, 2023. p. 352-367 (Lecture Notes in Computer Science; Vol. 14176).

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

TY - GEN

T1 - EmoKnow

T2 - 19th International Conference on Advanced Data Mining and Applications, ADMA 2023

AU - Zhang, Yuchen

AU - Su, Xing

AU - Wu, Jia

AU - Yang, Jian

AU - Fan, Hao

AU - Zheng, Xiaochuan

PY - 2023

Y1 - 2023

N2 - Content-based methods are inadequate for detecting fake news related to COVID-19 due to the complexity of this domain. Some studies integrate the social context information of the news to improve performance. However, such information is not consistently available and sometimes not helpful regarding COVID-19, as most users lack professional knowledge about it and may be unable to respond accurately. Additionally, fake news often employs emotional manipulation to exploit people’s emotions to shape their beliefs and actions. Therefore, we propose EmoKnow, an emotion- and knowledge-oriented model, for detecting fake news about COVID-19. Our proposed method incorporates language modeling, emotion feature extraction, and external knowledge sources to provide an informative representation of news. Experimental results on four COVID-19-related datasets show that EmoKnow significantly outperforms state-of-the-art approaches.

AB - Content-based methods are inadequate for detecting fake news related to COVID-19 due to the complexity of this domain. Some studies integrate the social context information of the news to improve performance. However, such information is not consistently available and sometimes not helpful regarding COVID-19, as most users lack professional knowledge about it and may be unable to respond accurately. Additionally, fake news often employs emotional manipulation to exploit people’s emotions to shape their beliefs and actions. Therefore, we propose EmoKnow, an emotion- and knowledge-oriented model, for detecting fake news about COVID-19. Our proposed method incorporates language modeling, emotion feature extraction, and external knowledge sources to provide an informative representation of news. Experimental results on four COVID-19-related datasets show that EmoKnow significantly outperforms state-of-the-art approaches.

KW - COVID-19

KW - Fake News Detection

KW - Text Embedding

KW - Emotion Features

KW - Knowledge Representation

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UR - http://purl.org/au-research/grants/arc/DP230100899

UR - http://purl.org/au-research/grants/arc/DE200100964

UR - http://purl.org/au-research/grants/arc/LP210301259

U2 - 10.1007/978-3-031-46661-8_24

DO - 10.1007/978-3-031-46661-8_24

M3 - Conference proceeding contribution

AN - SCOPUS:85177048301

SN - 9783031466601

T3 - Lecture Notes in Computer Science

SP - 352

EP - 367

BT - Advanced Data Mining and Applications

A2 - Yang, Xiaochun

A2 - Suhartanto, Heru

A2 - Wang, Guoren

A2 - Wang, Bin

A2 - Jiang, Jing

A2 - Li, Bing

A2 - Zhu, Huaijie

A2 - Cui, Ningning

PB - Springer, Springer Nature

CY - Cham

Y2 - 21 August 2023 through 23 August 2023

ER -

Zhang Y, Su X, Wu J , Yang J, Fan H, Zheng X. EmoKnow: emotion- and knowledge-oriented model for COVID-19 fake news detection. In Yang X, Suhartanto H, Wang G, Wang B, Jiang J, Li B, Zhu H, Cui N, editors, Advanced Data Mining and Applications: 19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, proceedings, part I. Cham: Springer, Springer Nature. 2023. p. 352-367. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-46661-8_24

EmoKnow: emotion- and knowledge-oriented model for COVID-19 fake news detection

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

DP230100899: New Graph Mining Technologies to Enable Timely Exploration of Social Events

Cite this

EmoKnow: emotion- and knowledge-oriented model for COVID-19 fake news detection

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

DP230100899: New Graph Mining Technologies to Enable Timely Exploration of Social Events

Cite this