Measuring public attitudes toward wildlife provides crucial insights into our
relationship with nature and helps monitor progress toward Global Biodiversity
Framework targets. Yet, conducting such assessments at a global scale is
challenging. Manually curating search terms for querying news and social media
is tedious, costly, and can lead to biased results. Raw news and social media
data returned from queries are often cluttered with irrelevant content and
syndicated articles. We aim to overcome these challenges by leveraging modern
Natural Language Processing (NLP) tools. We introduce a folk taxonomy approach
for improved search term generation and employ cosine similarity on Term
Frequency-Inverse Document Frequency vectors to filter syndicated articles. We
also introduce an extensible relevance filtering pipeline which uses
unsupervised learning to reveal common topics, followed by an open-source
zero-shot Large Language Model (LLM) to assign topics to news article titles,
which are then used to assign relevance. Finally, we conduct sentiment, topic,
and volume analyses on resulting data. We illustrate our methodology with a
case study of news and X (formerly Twitter) data before and during the COVID-19
pandemic for various mammal taxa, including bats, pangolins, elephants, and
gorillas. During the data collection period, up to 62% of articles including
keywords pertaining to bats were deemed irrelevant to biodiversity,
underscoring the importance of relevance filtering. At the pandemic's onset, we
observed increased volume and a significant sentiment shift toward horseshoe
bats, which were implicated in the pandemic, but not for other focal taxa. The
proposed methods open the door to conservation practitioners applying modern
and emerging NLP tools, including LLMs "out of the box," to analyze public
perceptions of biodiversity during current events or campaigns.

通过使用自然语言处理工具，本研究介绍了一种改进的搜索词生成方法并利用余弦相似度过滤文章，同时使用无监督学习和大型语言模型进行相关性筛选，以分析公众对野生动物的态度和全球生物多样性的感知，进而为保护活动提供支持。