Nuclear energy

Twitter data mining for social listening analysis

Bibliographic Data

ID16858998
AuthorsEnara Zarrabeitia Bilbao (0000-0002-2347-3885, University of the Basque Country, corresponding author), Enara Zarrabeitia-Bilbao, Maite Jaca-Madariaga (0000-0002-8848-0285, University of the Basque Country), Rosa-María Rio-Belver (0000-0002-4244-9098, University of the Basque Country), Rosa María Rio-Belver, Izaskun Alvarez-Meaza (0000-0002-2110-0719, University of the Basque Country)
Year2023
Volume13
Issue1
Pages29-29
Publication date2023-02-06
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSocial Network Analysis and Mining (JOURNAL)
Journal identifiersISSN: 1869-5450 • E-ISSN: 1869-5469
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s13278-023-01033-8
PMID36776143
OpenAlexW4319319340
LanguageEN
Citations received1
References cited39

Knowing the presence, attitude and sentiment of society is important to promote policies and actions that influence the development of different energy sources and even more so in the case of an energy source such as nuclear, which has not been without controversy in recent years. The purpose of this paper was to conduct a social listening analysis of nuclear energy using Twitter data mining. A total of 3,709,417 global tweets were analyzed through the interactions and emotions of Twitter users throughout a crucial year: 6 months before and 6 months after the beginning of Russian invasion of Ukraine and the first attack on the Zaporizhzhia NPP. The research uses a novel approach to combine social network analysis methods with the application of artificial neural network models. The results reveal the digital conversation is influenced by the Russian invasion of Ukraine. However, tweets containing personal opinions of influential people also manage to enter the digital conversation, defining the magnitude and direction of the debate. The digital conversation is not constructed as a public argument. Generally, it is a conversation with non-polarized communities (politics, business, science and media); neither armed conflict or military threats against Zaporizhzhia NPP succeed in rousing anti-nuclear voices, even though these events do modify the orientation of the sentiment in the language used, making it more negative

Active listening · Data science · Natural language processing · Physics · Sentiment analysis · Social media · World Wide Web · Communication · Complex Network Analysis Techniques · Computer Science · Opinion Dynamics and Social Influence · Psychology · Public Relations and Crisis Communication

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