AUTHOR=Elgammal Naglaa , Mohamed Hadeer Ahmed , Halim Sara Abdel Latif Abdel , Abd ElRahman Fathia Sabry , Fawzy Hala Elalfy 
  
TITLE=Analyzing YouTube users’ comments on climate change issues presented through immersive mixed reality using sentiment analysis and network analysis tools
  
JOURNAL=Frontiers in Communication
  
VOLUME=Volume 10 - 2025
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2025.1657059
  
DOI=10.3389/fcomm.2025.1657059
  
ISSN=2297-900X
  
ABSTRACT=The use of modern technologies, including Immersive Mixed Reality (IMR) technologies, to present information on climate change has become essential due to their ability to simplify information and increase interaction with it. Hence, the current study aimed to explore YouTube users’ engagement with climate change issues presented using (IMR) technologies by using both sentiment analysis and network analysis tools. It adopted a mixed method methodology (quantitative and qualitative analysis), analyzing the five most-popular videos that utilized IMR to deliver environmental content on The Weather Channel on YouTube. A range of analytical software was used to analyze the data collected. Specifically, Communalytic was used to collect comments, Gephi was used to analyze social networks, and NetworkX in Python was employed to calculate engagement metrics such as degree centrality and network density. TextBlob and VADER were also employed to analyze sentiment and classify comments as positive, negative, or neutral. Additionally, data analysis was used to study engagement dynamics within comments, analyze the evolution of engagement over time, and classify comment patterns based on writing style. The results showed that videos depicting severe weather events achieved the highest engagement rates, reflecting the emotional impact of the content on the audience. Social network analysis results indicated that most engagement was concentrated in a limited number of comments. Sentiment analysis revealed variations between analysis tools. VADER shows greater sensitivity to negative sentiment than TextBlob, underscoring the importance of using multiple analysis tools to ensure classification accuracy.