Module 1: Understanding sustainability communication content

AI-aided content analysis of sustainability communication

Lesson 1.1: Sustainability communication content

lecture text

Sustainability communication content

To understand sustainability communication, we need to define content both formally and functionally. Formally, we can consider sustainability content as any material that aligns with themes of environmental or social responsibility, such as climate action, resource use, or equity. Functionally, sustainability communication serves organizational purposes—such as shaping brand identity, complying with regulations, or engaging stakeholders. These functions affect how audiences perceive credibility and authenticity. Analyzing content through both lenses helps clarify its structure, intent, and potential impact.

Online content sampling

When collecting online sustainability communication for research, sampling methods shape the validity and reliability of findings. Random sampling is considered the gold standard in social science, but it may be difficult to apply in digital contexts. Instead, purposive or strategic sampling can ensure the inclusion of relevant or contrasting cases. Whichever method is used, researchers must assess whether their sample supports generalizable claims. A strong sampling design ensures that observed patterns are not merely artifacts of selection bias.

Expectations about sustainability communication

Sustainability communication can be studied using qualitative, quantitative, or mixed methods. Qualitative approaches explore the deeper meanings behind content, while quantitative methods enable comparisons and statistical testing. Descriptive studies reveal patterns and practices, while explanatory studies focus on why these patterns occur. For example, a linguistic comparison between high-impact and low-impact organizations might reveal different rhetorical strategies or terminology use. The process begins by formulating clear research questions and operationalizing them into testable hypotheses or coding schemes.

Find online sustainability communication

The first step in collecting sustainability communication is to select which organizations to study, guided by your research questions. You may focus on a single organization for depth or compare contrasting organizations for breadth. Official websites are a primary source of curated, strategic communication. Within these sites, researchers can collect the URLs of sustainability-related pages, often found under sections like “Sustainability,” “CSR,” or “Environment.” These links will form the basis of your content collection and further analysis.

Convert multimodal web pages to PDF

To capture complex sustainability content online, one effective method is to print web pages to PDF using a browser. This approach preserves multimodal elements such as layout, images, and embedded links, which are essential for analyzing how messages are visually and textually constructed. It’s a fast and accessible technique that requires no special tools. However, a limitation is that videos or dynamic elements are not captured, making this best suited for static page content.

Setting up an initial content database

After collecting the content, it’s important to store it in a way that supports reproducibility and easy access. Saving files locally is straightforward and works offline, but can be harder to share or back up. Using cloud platforms like Google Drive or GitHub offers better collaboration, version control, and accessibility. Regardless of the method, the goal is to make communication content systematically available for analysis, and to ensure it can be traced back to its source.

Organize communication content in folders

Organizing files into folders by organization or theme helps compare sustainability messaging across cases. Consistent file path naming is important for automating the analysis and maintaining structure. If content is saved as rendered PDFs, it may need to be converted into plain text later for textual analysis or coding. A clear folder structure simplifies navigation, supports reproducibility, and ensures that data management doesn’t become a barrier to insight.