[Paper] Automating Historical Insight Extraction from Large-Scale Newspaper Archives via Neural Topic Modeling

Source: arXiv - 2512.11635v1

Overview

The paper presents a new way to mine themes from massive historical newspaper collections using BERTopic, a neural topic‑model that builds on transformer embeddings. By applying it to over six decades of articles about nuclear power and safety, the authors show how modern NLP can reveal the rise, fall, and transformation of public discourse—something traditional models like LDA struggle to do.

Key Contributions

• Neural topic modeling for archives – First large‑scale application of BERTopic to noisy, OCR‑derived newspaper text spanning 1955‑2018.
• Temporal topic tracking – Introduces a pipeline for visualizing how specific themes (e.g., nuclear weapons vs. civilian nuclear energy) evolve over time.
• Noise‑robust preprocessing – Demonstrates practical steps to mitigate OCR errors and preserve semantic quality for transformer embeddings.
• Comparative evaluation – Benchmarks BERTopic against LDA and other baseline models, highlighting superior coherence and interpretability on historical data.
• Open‑source toolkit – Releases the full preprocessing, modeling, and visualization code, enabling reproducibility for other archival domains.

Methodology

1. Data collection & cleaning – The authors scraped digitized newspaper articles, applied language detection, removed boilerplate, and used spelling‑correction heuristics to reduce OCR artifacts.
2. Embedding generation – Each article is encoded with a pre‑trained multilingual transformer (e.g., sentence‑bert), turning raw text into dense vectors that capture context despite noisy input.
3. Dimensionality reduction – Uniform Manifold Approximation and Projection (UMAP) compresses the high‑dimensional embeddings while preserving local topic structure.
4. Clustering – HDBSCAN groups the reduced vectors into dense clusters; each cluster corresponds to a candidate “topic”.
5. Topic representation – For every cluster, the most representative words are extracted using class‑based TF‑IDF (c‑TF‑IDF), producing human‑readable labels.
6. Temporal analysis – Articles are timestamped; topic prevalence is aggregated per year, enabling trend lines and heat‑maps that illustrate how discourse shifts.
7. Baseline comparison – Parallel LDA runs on the same corpus provide a reference for topic coherence (via UMass and CV scores) and interpretability.

Results & Findings

• Higher coherence – BERTopic achieved a CV coherence score of 0.48, compared to 0.31 for LDA, indicating more semantically consistent topics.
• Dynamic theme discovery – Early years (1950s‑60s) show dominant topics around “nuclear weapons testing” and “Cold War fear,” while the 1970s‑80s bring “nuclear safety regulations” and “energy crises.”
• Co‑occurrence insights – The model uncovered periods where discussions of nuclear power and nuclear weapons overlapped (e.g., post‑Chernobyl), suggesting public anxiety linking civilian and military nuclear issues.
• Scalability – Processing ~1.2 M articles took ~12 hours on a single GPU, demonstrating feasibility for nation‑wide archives.
• Qualitative validation – Historians reviewing the top‑10 topics confirmed that the extracted themes matched known historical narratives and even surfaced lesser‑known sub‑topics (e.g., “nuclear waste transport routes”).

Practical Implications

• Digital humanities pipelines – Researchers can adopt the released BERTopic workflow to explore other archival corpora (e.g., legislative records, social media histories) without deep ML expertise.
• Media monitoring & risk analysis – Companies tracking long‑term sentiment around regulated technologies (nuclear, AI, biotech) can use the temporal topic tracking to anticipate policy shifts or public backlash.
• Search & discovery tools – News aggregators can enrich their indexing with neural topics, enabling users to browse archives by evolving themes rather than static keywords.
• Policy‑making support – Governments can quickly surface historical precedents for current debates (e.g., public reaction to nuclear plant proposals) to inform stakeholder engagement strategies.
• Improved OCR pipelines – The paper’s noise‑reduction tricks (character‑level language models, spelling correction) can be incorporated into any digitization workflow to boost downstream NLP performance.

Limitations & Future Work

• OCR dependency – Despite preprocessing, residual OCR errors still affect embedding quality, especially for older, low‑resolution scans.
• Transformer bias – The pre‑trained language model was not fine‑tuned on historical language, which may under‑represent archaic terminology.
• Granularity trade‑off – HDBSCAN’s density‑based clustering can merge distinct but low‑frequency topics, potentially hiding niche narratives.
• Future directions – The authors suggest fine‑tuning transformers on period‑specific corpora, integrating multimodal data (photos, advertisements), and exploring hierarchical topic models to capture sub‑theme structures.

Authors

• Keerthana Murugaraj
• Salima Lamsiyah
• Marten During
• Martin Theobald

Paper Information

• arXiv ID: 2512.11635v1
• Categories: cs.CL, cs.AI, cs.IR
• Published: December 12, 2025
• PDF: Download PDF