Abstract
Extracting coherent and human-understandable themes from large collections of unstructured historical newspaper archives presents significant challenges due to topic evolution, Optical Character Recognition (OCR) noise, and the sheer volume of text. Traditional topic-modeling methods, such as Latent Dirichlet Allocation (LDA), often fall short in capturing the complexity and dynamic nature of discourse in historical texts. To address these limitations, we employ BERTopic. This neural topic-modeling approach leverages transformerbased embeddings to extract and classify topics, which, despite its growing popularity, still remains underused in historical research. Our study focuses on articles published between 1955 and 2018, specifically examining discourse on nuclear power and nuclear safety. We analyze various topic distributions across the corpus and trace their temporal evolution to uncover long-term trends and shifts in public discourse. This enables us to more accurately explore patterns in public discourse, including the co-occurrence of themes related to nuclear power and nuclear weapons and their shifts in topic importance over time. Our study demonstrates the scalability and contextual sensitivity of BERTopic as an alternative to traditional approaches, offering richer insights into historical discourses extracted from newspaper archives. These findings contribute to historical, nuclear, and social-science research while reflecting on current limitations and proposing potential directions for future work.
Abstract (translated)
从大量未结构化的历史报纸档案中提取连贯且易于人类理解的主题面临着重大挑战，这些挑战包括主题演变、光学字符识别（OCR）噪声以及海量文本数据。传统的主题建模方法，如潜在狄利克雷分配（LDA），在捕捉历史性文献复杂性和动态性方面往往表现不足。为了克服这一局限性，我们采用BERTopic进行研究。这种方法利用基于变压器的嵌入技术来提取和分类主题，尽管其日益流行，在历史研究中仍被广泛忽视。我们的研究关注1955年至2018年间发表的文章，特别是关于核能与核电安全方面的讨论。通过分析整个语料库中的各种主题分布及其时间演变，我们揭示了长期趋势以及公共话语的转变，并能够更准确地探索公众讨论模式，包括相关主题（如核能和核武器）的同时出现及其随时间变化的主题重要性。 我们的研究展示了BERTopic在处理历史文献方面的可扩展性和上下文敏感度，为传统方法提供了一种替代方案。这为我们提供了对从报纸档案中提取的历史话语的更深层次理解。这些发现不仅有助于历史、核能和社会科学的研究，而且还反思了当前限制，并提出了未来工作的潜在方向。
URL
https://arxiv.org/abs/2512.11635
