mlsanityData to 14 Oct 2026

NewsPanda: Media Monitoring for Timely Conservation Action

arXiv 2305.01503 ↗First posted 30 Apr 2023cs.IR cs.CL cs.CYcited by 4

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Abstract (arXiv)

Non-governmental organizations for environmental conservation have a significant interest in monitoring conservation-related media and getting timely updates about infrastructure construction projects as they may cause massive impact to key conservation areas. Such monitoring, however, is difficult and time-consuming. We introduce NewsPanda, a toolkit which automatically detects and analyzes online articles related to environmental conservation and infrastructure construction. We fine-tune a BERT-based model using active learning methods and noise correction algorithms to identify articles that are relevant to conservation and infrastructure construction. For the identified articles, we perform further analysis, extracting keywords and finding potentially related sources. NewsPanda has been successfully deployed by the World Wide Fund for Nature teams in the UK, India, and Nepal since February 2022. It currently monitors over 80,000 websites and 1,074 conservation sites across India and Nepal, saving more than 30 hours of human efforts weekly. We have now scaled it up to cover 60,000 conservation sites globally.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 7 of its 21 references that are in the map. First posted on arXiv 30 Apr 2023.

Builds on

7 of 21 references.

References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  4. 2020Learning with Instance-Dependent Label Noise: A Sample Sieve Approachcited by 158
  5. 2019Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Ratescited by 142
  6. 2020Analyzing Sustainability Reports Using Natural Language Processingcited by 18
  7. 2016Stock trend prediction using news sentiment analysiscited by 9

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Citing papers per year: 2024: 3; 2025: 1.

4 papers.