Unraveling the Nuclear Debate: Insights Through Clustering of Tweets

The perception of nuclear power, while central to energy policy and sustainability endeavors, remains a subject of considerable debate, in which some claim that the expansion of nuclear technology poses threats to global security, while others argue that its access should be shared for development and energy purposes. In this study, a total of 11,256 tweets were gathered over a three-month period using a keyword-based approach through the Twitter Standard Search API, focusing on terms related to nuclear energy. The k-means clustering algorithm was employed to analyze tweets with the aim of determining the underlying sentiments and perspectives within the public domain, while t-SNE was used for visualizing cluster separation. The results show distinct clusters reflecting various viewpoints on nuclear power, with 71.94% of tweets being neutral, 14.64% supportive, and 13.42% negative. This study also identifies a subset of users who appear to be seeking unbiased information, signaling an opportunity for educational outreach. By leveraging the immediacy and pervasiveness of X (formerly known as Twitter), this research provides a timely snapshot of the prevailing attitudes toward nuclear power and offers insights for policymakers, educators, and industry stakeholders.

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Publication Details

Journal
Electronics
Published
2024-10-23
DOI
https://doi.org/10.3390/electronics13214159
Citations
2
Primary Topic
Risk Perception and Management
Type
article
Field-Weighted Citation Impact
2.26

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article

Unraveling the Nuclear Debate: Insights Through Clustering of Tweets

Sanja Seljan, Josip Katalinić, Ivan Đunđer
2 citations
Electronics
Risk Perception and Management
2.26
article

Unraveling the Nuclear Debate: Insights Through Clustering of Tweets

Sanja Seljan, Josip Katalinić, Ivan Đunđer
article en
2 citations

Abstract

The perception of nuclear power, while central to energy policy and sustainability endeavors, remains a subject of considerable debate, in which some claim that the expansion of nuclear technology poses threats to global security, while others argue that its access should be shared for development and energy purposes. In this study, a total of 11,256 tweets were gathered over a three-month period using a keyword-based approach through the Twitter Standard Search API, focusing on terms related to nuclear energy. The k-means clustering algorithm was employed to analyze tweets with the aim of determining the underlying sentiments and perspectives within the public domain, while t-SNE was used for visualizing cluster separation. The results show distinct clusters reflecting various viewpoints on nuclear power, with 71.94% of tweets being neutral, 14.64% supportive, and 13.42% negative. This study also identifies a subset of users who appear to be seeking unbiased information, signaling an opportunity for educational outreach. By leveraging the immediacy and pervasiveness of X (formerly known as Twitter), this research provides a timely snapshot of the prevailing attitudes toward nuclear power and offers insights for policymakers, educators, and industry stakeholders.

ElectronicsVol. 13(21)
University of Zagreb (HR)
Sveučilište u Zagrebu
Openalex Percentile: Top 10%
Risk Perception and Management
2.26
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Unraveling the Nuclear Debate: Insights Through Clustering of Tweets — Sanja Seljan, Josip Katalinić, et al. · Electronics (2024) | TGRS Research Map | TGRS