An analytical and experimental study of the energy transition discourse on YouTube

Abstract

Energy production and management face significant political, economic, and environmental challenges, yet the rise in information consumption through social media undermines the availability of reliable knowledge to the general public. This study examines the ideas discussed in the energy transition content on YouTube, assesses the most effective methods of communicating knowledge and information to the general public, and identifies the most engaged audiences. We examine videos related to the subject, analysing the themes discussed, the language used, and the emotions conveyed on YouTube, linking language formality to user engagement. To test the relationship experimentally, we uploaded original content to YouTube through two mirror channels containing the same material but using different levels of language formality. Our results indicate that conversational content reaches a broader audience, but retention rates are higher on the academic channel beyond the initial video segments. Interest in the topic varies by viewer profile, with younger individuals and women showing greater engagement regardless of language style.

Figures (27)

• Figure 1: Importance of the words in the detected topics. Ranking and weight of the most important words by topic detected.
• Figure 2: Distribution of concepts by topic. Number of videos per concept in each topic, where the highlighted bars in blue represent values above the 75th percentile, topics in the top 25%.
• Figure 3: Concept network. Network between concepts based on the number of videos that share the attributed topic. The thickness of the links is determined by the number of concept videos assigned to the same topic, normalised by the number of possible pairs of videos from the two connected concepts. The links in blue correspond to the minimum spanning tree and the dot size to the concept outflow.
• Figure 4: Analysis of the polar sentiment and semantic areas of the transcripts. Concept with the highest value for each semantic area. Semantic areas are shown in the vertical axes, and the colour indicates the keyword with the highest value. Only semantic areas where a keyword has a significantly larger value than the rest are shown. The inset shows the average negative emotions as a function of positive emotions for each concept.
• Figure 5: Language formality. (a) Distribution of formality scores in the analysed data set. (b) Average formality of the videos for each of the concepts.