Nuclear energy: Twitter data mining for social listening analysis.

Area of Science:

• Social Sciences
• Energy Policy
• Computational Social Science

Background:

• Public perception and societal attitudes are crucial for energy policy development, especially for nuclear energy due to its controversial nature.
• Understanding public sentiment is vital for informed decision-making regarding energy sources.

Purpose of the Study:

• To conduct a social listening analysis of nuclear energy using Twitter data mining.
• To analyze public attitude and sentiment towards nuclear energy over a critical period.

Main Methods:

• Analysis of 3,709,417 global tweets covering the 6 months before and after the Russian invasion of Ukraine and the attack on Zaporizhzhia NPP.
• Combination of social network analysis methods with artificial neural network models.

Main Results:

• The digital conversation surrounding nuclear energy was influenced by the Russian invasion of Ukraine.
• Influential individuals' opinions significantly shaped the debate's direction and scale.
• Despite conflict and threats, anti-nuclear voices were not significantly mobilized, though negative sentiment increased.

Conclusions:

• Digital conversations on nuclear energy are often within non-polarized communities (politics, business, science, media).
• Major geopolitical events, including armed conflict, did not fundamentally alter the structure of the nuclear energy debate online.
• While sentiment shifted negatively during crises, the overall public discourse remained largely stable and non-confrontational.