Public Backlash against Corporate Greenwashing on YouTube : Combining Social Network Analysis and Sentiment Detection = 유튜브에서의 기업 그린워싱에 대한 공중 반발: 사회연결망 분석과 감성 분석

다국어 초록 (Multilingual Abstract)

This study examines public backlash against Shell’s corporate greenwashing on YouTube through social network analysis, Structural Topic Modeling （STM）, and BERT-based sentiment analysis. A total of 55 videos featuring Shell’s greenwashing-related content, along with 6,339 comments and 4,995 replies, were collected. The videos uploaded between January 1,2014 and March 31, 2025, and their accompanying comments were collected. The data were divided into three temporal networks: T1 network （pre-litigation）, T2 network （during litigation）, and T3 network （post-verdict） – corresponding to key periods of Shell’s climate lawsuit. The dominant video content varied across networks: T1 network （n = 7 videos） focused on early concerns about Shell’s climate-related practices, T2 network （n = 39 videos） featured heightened criticism of Shell’s environmentally harmful business operations, and T3 network （n = 9 videos） primarily included coverage of Shell’s legal victory in the climate lawsuit. The study highlights change in networked interactions. Content analysis classified user-generated video contents into five blaming frames （emotional, factual, sarcastic, anti- corporate, non-blaming）. STM analysis of comments under each blaming frame revealed frame-specific topics-e.g., “government oil greed” （emotional）, “global climate problem” （factual）, “planet saving sarcasm” （sarcastic）, “energy market interest” （anti-corporate）, “human energy use” （non-blaming）-while sentiment analysis showed approval dominant in blaming frames videos comments while neutral and approval in non-blaming. This study provides insight into how public sentiment and backlash against greenwashing are shaped through online networks, offering implications for corporate reputation management and helping companies reduce backlash damage.

목차 (Table of Contents)

• Introduction 7
• Literature Review 10
• Greenwashing and Public Engagement 10
• Framing Theory and Attribution Theory 11
• Public Backlash on Social Media Platforms 11
• Research Questions 12
• Methods 13
• Data Collection 13
• Data Analysis 14
• Social Network Analysis 14
• Content analysis 15
• Topic modeling 16
• Sentiment analysis 17
• Results 18
• User Network Structures and User Interaction 18
• User Engagement Video Framing Analysis 19
• Topic Modeling of YouTube Video Comments 20
• Sentiment Analysis of YouTube Video Comments 20
• Discussion 21
• Conclusion 24
• Appendix 26
• References 39
• 요약 45
• Acknowledgement 46