<![CDATA[Corporate sustainability communication has intensified as firms seek legitimacy amid rising stakeholder pressure and global sustainability agendas. However, the rapid growth of environmental marketing claims has increased concerns regarding deceptive sustainability communication or greenwashing. This study investigates the linguistic mechanisms underlying greenwashing by decoding deceptive green signaling and linguistic obfuscation in environmental marketing claims using Natural Language Processing. Despite increasing regulatory attention, greenwashing detection remains largely reactive and subjective, relying mainly on qualitative assessments. This study examines whether linguistic patterns and institutional signals can be used to explore the feasibility of NLP- Based greenwashing classification in corporate marketing narratives. This research introduces the concept of linguistic biomarkers of greenwashing, shifting the literature from perception-based evaluation toward exploratory NLP framework for greenwashing screening while integrating Signaling Theory and Construal Level Theory. The study analyzes 91 corporate environmental marketing claims using Natural Language Processing to extract linguistic features such as lexical vagueness and sentiment. Statistical tests and a Random Forest model were applied to identify linguistic patterns and predict greenwashing probability. Basic linguistic indicators such as word count and sentiment do not significantly distinguish greenwashing from authentic claims. However, environmental certification functions as a strong costly signal separating credible sustainability communication. Greenwashing detection requires combining textual analysis with institutional credibility signals. The study provides a computational framework for detecting deceptive sustainability communication.]]>
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