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.

翻译：人象冲突(HEC)在印度日益加剧——栖息地丧失与人类定居点扩张迫使大象与人类更近距离接触。尽管冲突的生态驱动因素已得到深入研究，但新闻媒体对其的呈现方式仍鲜有探讨。本研究首次对印度媒体构建人象冲突叙事框架展开大规模计算分析，通过考察某主要英文媒体2022年1月至2025年9月间发表的1968篇全文新闻文章(含28986个句子)，采用融合长上下文变换器、大型语言模型和领域专项"负面大象描绘词汇表"的多模型情感分析框架，量化情感倾向、提取论证支撑句，并识别导致大象负面形象的语词模式。研究发现，恐惧诱导与攻击性相关语言占据主导地位。鉴于媒体叙事框架可塑造公众对野生动物及保护政策的态度，此类叙事倾向可能加剧公众敌意，削弱人象共存成效。本研究通过提供透明可扩展的方法论，并通过匿名化存储库公开全部资源，揭示了网络规模文本分析如何助力负责任的野生动物报道，促进具有社会效益的传媒实践。