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This paper analyzes 1,968 English-language news articles about human-elephant conflict (HEC) in India using a multi-model sentiment analysis framework combining transformers, LLMs, and a custom lexicon. The study quantifies sentiment and identifies linguistic patterns, revealing a prevalence of fear-inducing and aggression-related language in the media's portrayal of elephants. This negative framing risks exacerbating public hostility and hindering coexistence efforts.
Media coverage of human-elephant conflict in India overwhelmingly uses fear-inducing language, potentially undermining conservation efforts.
Human-elephant conflict (HEC) is rising across India as habitat loss and expanding human settlements force elephants into closer contact with people. While the ecological drivers of conflict are well-studied, how the news media portrays them remains largely unexplored. This work presents the first large-scale computational analysis of media framing of HEC in India, examining 1,968 full-length news articles consisting of 28,986 sentences, from a major English-language outlet published between January 2022 and September 2025. Using a multi-model sentiment framework that combines long-context transformers, large language models, and a domain-specific Negative Elephant Portrayal Lexicon, we quantify sentiment, extract rationale sentences, and identify linguistic patterns that contribute to negative portrayals of elephants. Our findings reveal a dominance of fear-inducing and aggression-related language. Since the media framing can shape public attitudes toward wildlife and conservation policy, such narratives risk reinforcing public hostility and undermining coexistence efforts. By providing a transparent, scalable methodology and releasing all resources through an anonymized repository, this study highlights how Web-scale text analysis can support responsible wildlife reporting and promote socially beneficial media practices.
