Towards Understanding Greenwashing in Social Media

Consumers today are becoming increasingly aware of the environmental impact of their purchases, leading them to seek out brands that genuinely prioritize sustainability. Social media plays a crucial role in influencing consumer decisions, as users turn to platforms like Facebook, Instagram, and YouTube for product recommendations. However, as demand for eco-friendly products grows, some companies exploit this trend by misleading consumers with exaggerated or false sustainability claims—a practice known as greenwashing. This deceptive tactic distorts the market, undermines consumer trust, and creates an uneven playing field where companies that make real environmental efforts struggle to stand out. Given the dominant role of digital marketing in today’s consumer landscape, greenwashing on social media has become a particularly pressing issue, necessitating research to expose deceptive practices and promote transparency in environmental marketing.

This project aims to analyze the prevalence of eco-friendly and greenwashing campaigns on social media, focusing on large corporations listed in the Fortune 500, as well as other brands that actively promote green messages online. The research will seek to identify which industries and companies are most likely to engage in green marketing or greenwashing, and it will assess the communication strategies they employ. Specifically, the project will examine which social media platforms are preferred for green marketing, what types of content (text, video, image) are used, and whether such content is sponsored. The project will also investigate the extent to which social media users can distinguish between genuine and misleading sustainability claims.

To achieve these objectives, the study will rely on advanced natural language processing (NLP) techniques, including text analysis and topic modeling, to detect environment-related content. To differentiate genuine eco-friendly messages from greenwashing, NLP-based classification models will be developed. A key step in this process will involve manually labeling a dataset of sustainability-related marketing messages to train and test these models. The study will leverage cutting-edge NLP methods, such as large language models (LLMs), to enhance the accuracy of greenwashing detection and compare their effectiveness against existing methodologies.

In addition to analyzing corporate messaging, the project will assess how social media users react to green marketing campaigns. Since official APIs do not grant full access to user engagement data on all platforms, the study will focus on marketing videos shared on YouTube. It will examine engagement metrics such as views, likes, and comments to measure how audiences respond to both authentic and misleading sustainability claims. By conducting sentiment analysis and stance detection on user comments, the project will assess whether users recognize greenwashing, whether they discuss or challenge misleading claims, and how these discussions evolve in online communities.

The expected outcomes of this research are threefold. First, it will provide empirical insights into how sustainability is communicated across different industries, offering a clearer picture of the various green marketing strategies used by corporations. Second, it will develop innovative tools for detecting green marketing and greenwashing content in social media advertisements at scale. Third, the project will offer valuable insights into consumer perceptions and reactions to sustainability claims, informing policy recommendations aimed at improving transparency in digital advertising. These findings will support efforts to prevent deceptive green marketing practices and promote corporate responsibility, contributing to a more trustworthy marketplace for sustainable products.