Deciphering Public Opinion of Nuclear Energy on Twitter

Aparup Khatua; Erik Cambria; Shirley S. Ho; Jin Cheon Na

doi:10.1109/IJCNN48605.2020.9206903

Deciphering Public Opinion of Nuclear Energy on Twitter

Aparup Khatua, Erik Cambria, Shirley S. Ho, Jin Cheon Na

Nanyang Technological University

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

16 Citations (Scopus)

Abstract

This paper explores nuclear energy-related Twitter discussions as a response to the 2011 Fukushima Nuclear Disaster and the 2017 Nobel Peace Prize won by the International Campaign to Abolish Nuclear Weapons. We have considered a total of 2 million tweets for these two events. In particular, we employed CNN, LSTM, and Bi-LSTM to investigate whether social media users are supportive or cynical about nuclear energy. Our AI algorithms have performed better for polarity detection (accuracy in the range of 90%) with respect to subjectivity detection (accuracy in the range of 75%). We also note that dominant aspects of supporting tweets revolve around concepts like clean energy, lower CO2 emission, and sustainable future. On the contrary, cynical users see nuclear energy as a threat to the environment, human life, and safety.

Original language	English
Title of host publication	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728169262
DOIs	https://doi.org/10.1109/IJCNN48605.2020.9206903
Publication status	Published - Jul 2020
Externally published	Yes
Event	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom Duration: Jul 19 2020 → Jul 24 2020

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/Territory	United Kingdom
City	Virtual, Glasgow
Period	7/19/20 → 7/24/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

ASJC Scopus Subject Areas

Software
Artificial Intelligence

Keywords

artificial intelligence
nuclear energy
public opinion mining

Access to Document

10.1109/IJCNN48605.2020.9206903

Cite this

Khatua, A., Cambria, E., Ho, S. S., & Na, J. C. (2020). Deciphering Public Opinion of Nuclear Energy on Twitter. In 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings Article 9206903 (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN48605.2020.9206903

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title = "Deciphering Public Opinion of Nuclear Energy on Twitter",

abstract = "This paper explores nuclear energy-related Twitter discussions as a response to the 2011 Fukushima Nuclear Disaster and the 2017 Nobel Peace Prize won by the International Campaign to Abolish Nuclear Weapons. We have considered a total of 2 million tweets for these two events. In particular, we employed CNN, LSTM, and Bi-LSTM to investigate whether social media users are supportive or cynical about nuclear energy. Our AI algorithms have performed better for polarity detection (accuracy in the range of 90%) with respect to subjectivity detection (accuracy in the range of 75%). We also note that dominant aspects of supporting tweets revolve around concepts like clean energy, lower CO2 emission, and sustainable future. On the contrary, cynical users see nuclear energy as a threat to the environment, human life, and safety.",

keywords = "artificial intelligence, nuclear energy, public opinion mining",

author = "Aparup Khatua and Erik Cambria and Ho, {Shirley S.} and Na, {Jin Cheon}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Joint Conference on Neural Networks, IJCNN 2020 ; Conference date: 19-07-2020 Through 24-07-2020",

year = "2020",

month = jul,

doi = "10.1109/IJCNN48605.2020.9206903",

language = "English",

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Khatua, A, Cambria, E, Ho, SS & Na, JC 2020, Deciphering Public Opinion of Nuclear Energy on Twitter. in 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings., 9206903, Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., 2020 International Joint Conference on Neural Networks, IJCNN 2020, Virtual, Glasgow, United Kingdom, 7/19/20. https://doi.org/10.1109/IJCNN48605.2020.9206903

Deciphering Public Opinion of Nuclear Energy on Twitter. / Khatua, Aparup; Cambria, Erik; Ho, Shirley S. et al.
2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9206903 (Proceedings of the International Joint Conference on Neural Networks).

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

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AU - Khatua, Aparup

AU - Cambria, Erik

AU - Ho, Shirley S.

AU - Na, Jin Cheon

PY - 2020/7

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N2 - This paper explores nuclear energy-related Twitter discussions as a response to the 2011 Fukushima Nuclear Disaster and the 2017 Nobel Peace Prize won by the International Campaign to Abolish Nuclear Weapons. We have considered a total of 2 million tweets for these two events. In particular, we employed CNN, LSTM, and Bi-LSTM to investigate whether social media users are supportive or cynical about nuclear energy. Our AI algorithms have performed better for polarity detection (accuracy in the range of 90%) with respect to subjectivity detection (accuracy in the range of 75%). We also note that dominant aspects of supporting tweets revolve around concepts like clean energy, lower CO2 emission, and sustainable future. On the contrary, cynical users see nuclear energy as a threat to the environment, human life, and safety.

AB - This paper explores nuclear energy-related Twitter discussions as a response to the 2011 Fukushima Nuclear Disaster and the 2017 Nobel Peace Prize won by the International Campaign to Abolish Nuclear Weapons. We have considered a total of 2 million tweets for these two events. In particular, we employed CNN, LSTM, and Bi-LSTM to investigate whether social media users are supportive or cynical about nuclear energy. Our AI algorithms have performed better for polarity detection (accuracy in the range of 90%) with respect to subjectivity detection (accuracy in the range of 75%). We also note that dominant aspects of supporting tweets revolve around concepts like clean energy, lower CO2 emission, and sustainable future. On the contrary, cynical users see nuclear energy as a threat to the environment, human life, and safety.

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KW - public opinion mining

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M3 - Conference contribution

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T3 - Proceedings of the International Joint Conference on Neural Networks

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Deciphering Public Opinion of Nuclear Energy on Twitter

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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