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Updated: Jan 20, 2026
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自动化从数字媒体对生物多样性的公众突出性和态度的分析
Noah Giebink1, Amrita Gupta1, Diogo Veríssimo2
1Conservation Science Partners, Inc., Truckee, California, USA.
概括
这项研究引入了一种使用民间分类学和大型语言模型 (LLM) 的新方法,以分析数字媒体对野生动物的公众态度. 该方法有效地过不相关的内容,使生物多样性感知得到更好的监测.
科学领域:
- 计算社会科学 计算社会科学
- 生物多样性保护 生物多样性保护
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 全球监测公众对野生动物的态度对于生物多样性目标至关重要.
- 分析野生动物态度的数字媒体面临着各种物种命名法和无关内容等挑战.
- 现有的方法往往需要大量的手册注释或集体特定的培训.
研究的目的:
- 开发和验证一种自动化方法,通过数字新闻和社交媒体分析公众对野生动物的态度.
- 解决搜索术语选择和内容相关性过方面的挑战.
- 应用NLP工具,包括大型语言模型 (LLM),用于可扩展的公众认知分析.
主要方法:
- 利用民间分类学方法,以基于常见名称末尾的有效搜索术语生成.
- 实现了与术语频率-反向文档频率 (TF-IDF) 矢量的共因相似性,以识别联合文章.
- 开发了一种两阶段的相关性过器,采用无监督学习来发现主题和零射击的LLM来分配主题和估计相关性.
主要成果:
- 相关性过器成功删除了高达62%的无关文章,证明了其有效性.
- 对关于蝙蝠,松鼠,大象和大猩猩的新闻和X (以前是Twitter) 帖子的分析显示了大量和情绪的显著变化,特别是在COVID-19大流行开始时.
- 在2020年1月提到马蝙蝠的文章的显著增加被观察到,随后在2020年10月的情绪转变.
结论:
- 开发的NLP方法为分析公众对生物多样性的看法提供了一种实用且可扩展的解决方案.
- 这种方法有效地利用现代的LLM和无监督学习来克服传统方法的局限性.
- 这些发现强调了数字媒体分析对跟踪公众对野生动物和保护工作的情绪的有用性.
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