Why Is This Green? LLM-Based Explanations of Implicit Green Practices in Social Media

Abstract

Identifying green practices in social media is not merely a matter of lexical matching. Many green practices are expressed implicitly, rely on shared background knowledge, or are embedded in broader contextual narratives. In this paper, we investigate how large language models (LLMs) explain expert annotations of green waste management practices and how they rationalize classification errors made by a fine-tuned model (mBART) on a Russian social media corpus (GreenRu). We analyze explanations generated by two LLMs (T-lite and GigaChat) in two settings: (1) explaining gold expert-assigned labels and (2) interpreting erroneous model predictions. Our qualitative and micro-quantitative analysis shows that green practices are frequently inferred through contextual reasoning rather than explicit terminology. Error patterns of mBART reveal overgeneralization, associative misinterpretation (e.g., linking food sharing to waste recycling), and detection of practices where none are present. We further compare explanatory strategies of the two LLMs. T-lite tends to rely on lexical cues and surface markers that may create an impression of a practice, while GigaChat more often reconstructs broader contextual interpretations. Expert feedback highlights limitations of formal textual analysis, sensitivity to missing contextual knowledge, and difficulties in aligning model reasoning with expert conceptual boundaries. Our findings suggest that explanation-based analysis is a productive tool for diagnosing classification errors and refining annotation guidelines. More broadly, the study demonstrates that modeling implicit sustainability discourse requires contextual grounding and deeper semantic integration beyond keyword-based approaches.

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