Unraveling the Nuclear Debate: Insights Through Clustering of Tweets

• Published in: Electronics (Basel) Vol. 13; no. 21; p. 4159
• Main Authors: Katalinić, Josip, Dunđer, Ivan, Seljan, Sanja
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2024
• Subjects: Algorithms; Analysis; Cluster analysis; Clustering; Data collection; Datasets; Energy policy; Energy resources; Keywords; Machine learning; Nuclear energy; Nuclear energy policy; Nuclear reactor components; Perceptions; Public domain; Public opinion; Sentiment analysis; Social networks; Sustainability; Trends; Vector quantization; Waste management; Japan
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics13214159

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.