How English Print Media Frames Human-Elephant Conflicts in India

2026-04-23

AI Summary

This study investigates how Indian English-language print media narratively construct human–elephant conflict and shape public attitudes and conservation policies. Drawing on a corpus of 1,968 news articles published between January 2022 and September 2025, the work proposes an innovative, scalable media analysis framework that uniquely integrates long-context Transformers, large language models, and a custom-built “negative elephant portrayal lexicon” to enable sentiment quantification and salient sentence extraction. Findings reveal that media coverage frequently employs fear-inducing and aggression-laden language, potentially exacerbating public antagonism and impeding coexistence efforts. The proposed framework establishes a reproducible paradigm for responsible wildlife journalism and includes a publicly released, anonymized dataset to support future research in this domain.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Computer Vision: Language and Vision
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

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.

Problem

• human-elephant conflict
• media framing
• sentiment analysis
• wildlife portrayal
• conservation communication

Innovation

• computational media framing
• multi-model sentiment analysis
• domain-specific lexicon
• large-scale text analysis
• wildlife conservation communication