Towards efficient and accessible geoparsing of UK local media: A benchmark dataset and LLM-based approach

Location mentions in local news are crucial for examining issues like spatial inequalities, news deserts, and the impact of media ownership on news diversity. However, while geoparsing—extracting and resolving location mentions—has advanced through statistical and deep learning methods, its use in local media studies remains limited and fragmented due to technical challenges and a lack of practical frameworks. To address these challenges, we identify key considerations for successful geoparsing and review spatially oriented local media studies, finding over-reliance on limited geospatial vocabularies, limited toponym disambiguation, and inadequate validation of methods. These findings underscore the need for adaptable and robust solutions, and recent advancements in fine-tuned Large Language Models (LLMs) for geoparsing offer a promising direction by simplifying technical implementation and excelling at understanding contextual nuances. However, their application to UK local media—marked by fine-grained geographies and colloquial place names—remains underexplored due to the absence of benchmark datasets. This gap hinders researchers’ ability to evaluate and refine geoparsing methods for this domain. To address this, we introduce the Local Media UK Geoparsing (LMUK-Geo) dataset, a hand-annotated corpus of UK local news articles designed to support the development and evaluation of geoparsing pipelines. We also propose an LLM-driven approach for toponym disambiguation that replaces fine-tuning with accessible prompt engineering. Using LMUK-Geo, we benchmark our approach against a fine-tuned method. Both perform well on the novel dataset: the fine-tuned model excels in minimising coordinate-error distances, while the prompt-based method offers a scalable alternative for district-level classification, particularly when relying on predictions agreed upon by multiple models. Our contributions establish a foundation for geoparsing local media, advancing methodological frameworks and practical tools to enable systematic and comparative research.

Abernathy, P. M. (2020). News deserts and ghost newspapers: Will local news survive? UNC Hussman School of Journalism
and Media.
Ardanuy, M. C., K. Hosseini, K. McDonough, A. Krause, D. Van Strien, and F. Nanni (2020). A deep learning approach
to geographical candidate selection through toponym matching. In Proceedings of the 28th International Conference on
Advances in Geographic Information Systems, pp. 385–388. ACM.
Ardanuy, M. C., F. Nanni, K. Beelen, and L. Hare (2023). The past is a foreign place: Improving toponym linking for
historical newspapers. In Workshop on Computational Humanities Research.
Ardanuy, M. C. and C. Sporleder (2017). Toponym disambiguation in historical documents using semantic and geographic
features. In Proceedings of the 2nd International Conference on Digital Access to Textual Cultural Heritage, DATeCH2017, pp.
175–180. Association for Computing Machinery.
Berragan, C., A. Singleton, A. Calafiore, and J. Morley (2023). Transformer based named entity recognition for place name
extraction from unstructured text. International Journal of Geographical Information Science 37 (4), 747–766.
Berragan, C., A. Singleton, A. Calafiore, and J. Morley (2024). Mapping cognitive place associations within the united
kingdom through online discussion on reddit. Transactions of the Institute of British Geographers 49(3), e12669.
Bisiani, S. (2024). UKTwitNewsCor. https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/R5XTEO.
Harvard Dataverse. Creative Commons Attribution Non Commercial 4.0 International. Accessed: 2025-01-20.
Bisiani, S., A. Gulyas, J. Wihbey, and B. Heravi (2025). Uktwitnewscor: A dataset of online local news articles for the study
of local news provision. https://arxiv.org/abs/2505.14326. Accessed: 2025-05-22.
Bisiani, S. and B. Heravi (2023). Uncovering the state of local news databases in the UK: Limitations and impacts on
research. Journalism and Media 4(4), 1211–1231.
Bisiani, S. and J. Mitchell (2024). Uk local news mapping report - april 2024. https://www.publicinterestnews.org.uk/
_files/ugd/cde0e9_31f2ee78fff64c3e8616e7eafdf28f99.pdf. Public Interest News Foundation. Accessed: 2024-12-20.
Bommasani, R., D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. v. Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill,
E. Brynjolfsson, S. Buch, D. Card, R. Castellon, N. Chatterji, A. Chen, K. Creel, J. Q. Davis, D. Demszky, C. Donahue,
M. Doumbouya, E. Durmus, S. Ermon, J. Etchemendy, K. Ethayarajh, L. Fei-Fei, C. Finn, T. Gale, L. Gillespie, K. Goel,
N. Goodman, S. Grossman, N. Guha, T. Hashimoto, P. Henderson, J. Hewitt, D. E. Ho, J. Hong, K. Hsu, J. Huang, T. Icard,
S. Jain, D. Jurafsky, P. Kalluri, S. Karamcheti, G. Keeling, F. Khani, O. Khattab, P. W. Koh, M. Krass, R. Krishna, R. Kuditipudi, A. Kumar, F. Ladhak, M. Lee, T. Lee, J. Leskovec, I. Levent, X. L. Li, X. Li, T. Ma, A. Malik, C. D. Manning,
S. Mirchandani, E. Mitchell, Z. Munyikwa, S. Nair, A. Narayan, D. Narayanan, B. Newman, A. Nie, J. C. Niebles, H. Nilforoshan, J. Nyarko, G. Ogut, L. Orr, I. Papadimitriou, J. S. Park, C. Piech, E. Portelance, C. Potts, A. Raghunathan,
R. Reich, H. Ren, F. Rong, Y. Roohani, C. Ruiz, J. Ryan, C. Ré, D. Sadigh, S. Sagawa, K. Santhanam, A. Shih, K. Srinivasan,
A. Tamkin, R. Taori, A. W. Thomas, F. Tramèr, R. E. Wang, W. Wang, B. Wu, J. Wu, Y. Wu, S. M. Xie, M. Yasunaga, J. You,
M. Zaharia, M. Zhang, T. Zhang, X. Zhang, Y. Zhang, L. Zheng, K. Zhou, and P. Liang (2022). On the opportunities and
risks of foundation models. http://arxiv.org/abs/2108.07258. arXiv. Accessed: 2025-01-13.
Brown, T., B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen,
E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei (2020).
Language models are few-shot learners. In Advances in Neural Information Processing Systems, Volume 33, pp. 1877–1901.
Curran Associates, Inc.
Cairncross, F. (2019). The cairncross review. a sustainable future for journalism. https://assets.publishing.service.gov.uk/
media/5c6bfcd4e5274a72b933311d/021919_DCMS_Cairncross_Review_.pdf. Accessed: 2025-01-13.
Chatrath, V., M. Lotif, and S. Raza (2024). Fact or fiction? can LLMs be reliable annotators for political truths? http:
//arxiv.org/abs/2411.05775. Accessed: 2025-01-13.
Dang, H., L. Mecke, F. Lehmann, S. Goller, and D. Buschek (2022). How to prompt? opportunities and challenges of zeroand few-shot learning for human-AI interaction in creative applications of generative models. http://arxiv.org/abs/
2209.01390. Accessed: 2025-01-13.
DeLozier, G., J. Baldridge, and L. London (2015). Gazetteer-independent toponym resolution using geographic word
profiles. In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 29(1).
Dubourg, E., V. Thouzeau, and N. Baumard (2024). A step-by-step method for cultural annotation by LLMs. Frontiers in
Artificial Intelligence 7.
Firmstone, J. and R. Whittington (2021). Local political journalism: Systematic pressures on the normative functions of
local news. In The Routledge Companion to Political Journalism, pp. 84–93. Routledge.
Franklin, B. (2006). Attacking the devil? local journalists and local newspapers in the UK. In Local Journalism and Local
Media: Making the Local News, pp. 3–15. Routledge.
Franklin, B. and S. Cushion (2006). Downgrading the ‘local’in local newspapers’ miscing of the 2005 UK general election.
In Local Journalism and Local Media, pp. 256–269. Routledge.
Gale, W. A., K. W. Church, and D. Yarowsky (1992). One sense per discourse. In Speech and Natural Language: Proceedings
of a Workshop Held at Harriman, New York, February 23-26, 1992.
Gao, P., C. Lee, and D. Murphy (2020). Financing dies in darkness? the impact of newspaper closures on public finance.
Journal of Financial Economics 135(2), 445–467.
Garz, M. and M. Ots (2025). Media consolidation and news content quality. Journal of Communication, jqae053.
Goel, A., A. Gueta, O. Gilon, C. Liu, S. Erell, L. H. Nguyen, X. Hao, B. Jaber, S. Reddy, R. Kartha, J. Steiner, I. Laish, and
A. Feder (2023). LLMs accelerate annotation for medical information extraction. In Proceedings of the 3rd Machine
Learning for Health Symposium, pp. 82–100. PMLR.
Gritta, M., M. T. Pilehvar, and N. Collier (2020). A pragmatic guide to geoparsing evaluation. Language Resources and
Evaluation 54(3), 683–712.
Downloaded from https://www.cambridge.org/core. 12 Oct 2025 at 13:12:56, subject to the Cambridge Core terms of use.
18 Simona Bisiani et al.
Gritta, M., M. T. Pilehvar, N. Limsopatham, and N. Collier (2018). What’s missing in geographical parsing? Language
Resources and Evaluation 52(2), 603–623.
Hagar, N., J. Bandy, D. Trielli, Y. Wang, and N. Diakopoulos (2020). Defining local news: A computational approach. Computational + Journalism Symposium 2020. Retrieved from: https://cj2021.northeastern.edu/files/2020/02/CJ_2020_paper_
40.pdf. Accessed: 2025-01-13.
Hayes, D. and J. L. Lawless (2018). The decline of local news and its effects: New evidence from longitudinal data. The
Journal of Politics 80(1), 332–336.
Hess, K. and L. Waller (2017). Local journalism in a digital world (First published ed.). Macmillan education. Palgrave
Macmillan.
Honnibal, M. and I. Montani (2017). spacy 2: Natural language understanding with bloom embeddings, convolutional
neural networks and incremental parsing.
Hu, X., T. Elßner, S. Zheng, H. N. Serere, J. Kersten, F. Klan, and Q. Qiu (2024). DLRGeoTweet: A comprehensive social
media geocoding corpus featuring fine-grained places. Information Processing & Management 61(4), 103742.
Hu, X., J. Kersten, F. Klan, and S. M. Farzana (2024). Toponym resolution leveraging lightweight and open-source large
language models and geo-knowledge. International Journal of Geographical Information Science, 1–28.
Hu, X., Y. Sun, J. Kersten, Z. Zhou, F. Klan, and H. Fan (2023). How can voting mechanisms improve the robustness and
generalizability of toponym disambiguation? International Journal of Applied Earth Observation and Geoinformation 117,
103191.
Hu, X., Z. Zhou, H. Li, Y. Hu, F. Gu, J. Kersten, H. Fan, and F. Klan (2023). Location reference recognition from texts: A
survey and comparison. ACM Computing Surveys 56(5), 112:1–112:37.
Hu, Y., G. Mai, C. Cundy, K. Choi, N. Lao, W. Liu, G. Lakhanpal, R. Z. Zhou, and K. Joseph (2023). Geo-knowledgeguided GPT models improve the extraction of location descriptions from disaster-related social media messages.
International Journal of Geographical Information Science 37 (11), 2289–2318. Publisher: Taylor & Francis _eprint:
https://doi.org/10.1080/13658816.2023.2266495.
Huang, L., W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin, and T. Liu (2024). A survey on
hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Trans. Inf. Syst. 43(2),
1–55.
Hunter, T. (2024). UK local newspaper closures update: 293 now gone since 2005. https://pressgazette.co.uk/publishers/
regional-newspapers/local-newspaper-closures-uk-2022-to-2024/. Press Gazette. Accessed: 2025-01-13.
Kafando, R., R. Decoupes, M. Roche, and M. Teisseire (2023). SNEToolkit: Spatial named entities disambiguation toolkit.
SoftwareX 23, 101480.
Kamalloo, E. and D. Rafiei (2018). A coherent unsupervised model for toponym resolution. In Proceedings of the 2018 World
Wide Web Conference on World Wide Web - WWW ’18, pp. 1287–1296. ACM Press.
Karimzadeh, M., S. Pezanowski, A. M. MacEachren, and J. O. Wallgrün (2019). GeoTxt: A scalable geoparsing system for
unstructured text geolocation. Transactions in GIS 23(1), 118–136.
Karjus, A. (2023). Machine-assisted mixed methods: augmenting humanities and social sciences with artificial intelligence.
http://arxiv.org/abs/2309.14379. Accessed: 2025-01-13.
Khanom, A., D. Kiesow, M. Zdun, and C.-R. Shyu (2023). The news crawler: A big data approach to local information
ecosystems. Media and Communication 11(3), 318–329.
Koetsenruijter, W. and J. De Jong (2023). The rhetoric of trust in local news media: Proximity as a quintessential news
quality. Rhetoric and Communications (55), 9–37.
Lee, S. Y.-T., A. Bahukhandi, D. Liu, and K.-L. Ma (2025). Towards dataset-scale and feature-oriented evaluation of text
summarization in large language model prompts. In IEEE Transactions on Visualization and Computer Graphics, Volume
31(1), pp. 481–491.
Leidner, J. L. (2007). Toponym resolution in text: annotation, evaluation and applications of spatial grounding. SIGIR
Forum 41(2), 124–126.
Leppämäki, T., T. Toivonen, and T. Hiippala (2024). Geographical and linguistic perspectives on developing geoparsers
with generic resources. International Journal of Geographical Information Science 38(10), 2039–2060.
Lewis, J., A. Williams, and B. Franklin (2008). Four rumours and an explanation: A political economic account of journalists’
changing newsgathering and miscing practices. Journalism Practice 2(1), 27–45.
Lewis, P., E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, et al.
(2020). Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing
Systems 33, 9459–9474.
Lieberman, M. D., H. Samet, and J. Sankaranarayanan (2010). Geotagging with local lexicons to build indexes for textuallyspecified spatial data. In 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010), pp. 201–212. ISSN:
2375-026X.
Lindgren, A. (2009). News, geography and disadvantage: Mapping newspaper coverage of high-needs neighbourhoods in
toronto, canada. Canadian Journal of Urban Research 18(1), 74–97.
Liu, F., Z. Kang, and X. Han (2025). Optimizing rag techniques for automotive industry pdf chatbots: A case study with
locally deployed ollama modelsoptimizing rag techniques based on locally deployed ollama modelsa case study with
locally deployed ollama models. In Proceedings of the 2024 3rd International Conference on Artificial Intelligence and
Intelligent Information Processing, AIIIP ’24, New York, NY, USA, pp. 152–159. Association for Computing Machinery.
Liu, P., W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig (2023). Pre-train, prompt, and predict: A systematic survey of
prompting methods in natural language processing. ACM Computing Surveys 55(9), 1–35.
Liu, Z., K. Janowicz, L. Cai, R. Zhu, G. Mai, and M. Shi (2022). Geoparsing: Solved or biased? an evaluation of geographic
biases in geoparsing. AGILE: GIScience Series 3, 1–13.
Madrid-Morales, D. (2020). Using computational text analysis tools to study african online news content. African Journalism
Studies 41(4), 68–82.
Downloaded from https://www.cambridge.org/core. 12 Oct 2025 at 13:12:56, subject to the Cambridge Core terms of use.
Computational Humanities Research 19
Madrid-Morales, D., J. R. Rodríguez-Amat, and P. Lindner (2023). A computational mapping of online news deserts on
african news websites. Media and Communication 11(3), 330–342.
Mai, G., W. Huang, J. Sun, S. Song, D. Mishra, N. Liu, S. Gao, T. Liu, G. Cong, Y. Hu, C. Cundy, Z. Li, R. Zhu, and N. Lao
(2024). On the opportunities and challenges of foundation models for GeoAI (vision paper). ACM Transactions on Spatial
Algorithms and Systems 10(2), 1–46.
Majid, A. (2023). Who owns UK local news media? print and digital consolidation charted. https://pressgazette.co.uk/
media-audience-and-business-data/who-owns-the\-uk-regional-media-print-and-digital/. Press Gazette. Accessed:
2024-12-27.
Matsuda, K., A. Sasaki, N. Okazaki, and K. Inui (2015). Annotating geographical entities on microblog text. In A. Meyers,
I. Rehbein, and H. Zinsmeister (Eds.), Proceedings of the 9th Linguistic Annotation Workshop, pp. 85–94. Association for
Computational Linguistics.
McAdam, A. and K. Hess (2024). Re-asserting the value of local “news presence” for small-town news outlets in a digital
era. Journalism Practice 0(0), 1–16.
Metzger, Z. (2024). The state of local news 2024. https://localnewsinitiative.northwestern.edu/projects/
state-of-local-news/2024/. Published by Local News Initiative, Medill School of Media, Journalism, and Integrated Marketing
Communications, Northwestern University. Accessed: 2025-01-12.
Middleton, S. E., G. Kordopatis-Zilos, S. Papadopoulos, and Y. Kompatsiaris (2018, June). Location extraction from social
media: Geoparsing, location disambiguation, and geotagging. ACM Trans. Inf. Syst. 36(4), 1–27.
Montani, I. and M. Honnibal (2018). Prodigy: A new annotation tool for radically efficient machine teaching. https:
//explosion.ai/blog/prodigy-annotation-tool-active-learning. Accessed: 2025-01-13.
Moore, M. and G. N. Ramsay (2024). Local news in national elections: An “audit” approach to assessing local news
performance during a national election campaign. Digital Journalism 0(0), 1–20.
Napoli, P. M. and M. S. Weber (2020). Local journalism and at-risk communities in the united states. In The Routledge
Companion to Local Media and Journalism, pp. 368–378. Routledge.
Pebesma, E. J. et al. (2018). Simple features for r: standardized support for spatial vector data. R J. 10(1), 439.
PLUM Consulting (2020). Research into recent dynamics of the press sector in the UK and globally. https:
//assets.publishing.service.gov.uk/media/5f7b4673e90e070dec5d9e29/Plum_DCMS_press_sector_dynamics_-_
Final_misc_v4.pdf. Accessed: 2025-01-13.
Ponsford, D. (2024). Colossal decline of UK regional media since 2007 revealed. https://pressgazette.co.uk/publishers/
regional-newspapers/colossal-decline-of-uk-regional-media-since-2007-revealed/. Press Gazette. Accessed: 2024-02-
28.
Public Interest News Foundation (2023). Deserts, oases and drylands. https://www.publicinterestnews.org.uk/_files/ugd/
cde0e9_97c4fe55ab0c49a0a29f40937e71d216.pdf. Accessed: 2023-10-06.
Quattrone, G., L. Capra, and P. De Meo (2015). There’s no such thing as the perfect map: Quantifying bias in spatial crowdsourcing datasets. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing,
CSCW ’15, pp. 1021–1032. Association for Computing Machinery.
Ramsay, G. and M. Moore (2016). Monopolising local news: Is there an emerging local democratic deficit in the UK due to
the decline of local newspapers? https://www.kcl.ac.uk/policy-institute/assets/cmcp/local-news.pdf. Publisher: King’s
College London.
Reese, S. D. (2016). The new geography of journalism research. Digital Journalism 4(7), 816–826.
Sanh, V., A. Webson, C. Raffel, S. H. Bach, L. Sutawika, Z. Alyafeai, A. Chaffin, A. Stiegler, T. L. Scao, A. Raja, M. Dey, M. S.
Bari, C. Xu, U. Thakker, S. S. Sharma, E. Szczechla, T. Kim, G. Chhablani, N. Nayak, D. Datta, J. Chang, M. T.-J. Jiang,
H. Wang, M. Manica, S. Shen, Z. X. Yong, H. Pandey, R. Bawden, T. Wang, T. Neeraj, J. Rozen, A. Sharma, A. Santilli,
T. Fevry, J. A. Fries, R. Teehan, T. Bers, S. Biderman, L. Gao, T. Wolf, and A. M. Rush (2022). Multitask prompted training
enables zero-shot task generalization. http://arxiv.org/abs/2110.08207.
Sharman, D. (2021). Reach plc to close all bar 15 of its newspaper offices. https://www.holdthefrontpage.co.uk/2021/
news/publisher-to-close-all-bar-15-offices-leaving-dailies-without-base-on-patch/. Hold The Front Page. Accessed:
2025-01-13.
Sjøvaag, H. (2014). Homogenisation or differentiation?: The effects of consolidation in the regional newspaper market.
Journalism Studies 15(5), 511–521.
Teitler, B. E., M. D. Lieberman, D. Panozzo, J. Sankaranarayanan, H. Samet, and J. Sperling (2008). NewsStand: a new
view on news. In Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information
systems, GIS ’08, pp. 1–10. Association for Computing Machinery.
The Media Reform Coalition (2023). Who owns the UK media? https://www.mediareform.org.uk/wp-content/uploads/
2023/10/Who-Owns-the-UK-Media-2023.pdf. Accessed: 2025-01-13.
Trad, F. and A. Chehab (2024). To ensemble or not: Assessing majority voting strategies for phishing detection with large
language models. http://arxiv.org/abs/2412.00166. Accessed: 2025-01-13.
Usher, N. (2019). Putting “place” in the center of journalism research: A way forward to understand challenges to trust and
knowledge in news. Journalism & Communication Monographs 21(2), 84–146.
Vogler, D., M. Weston, and L. Udris (2023). Investigating news deserts on the content level: Geographical diversity in swiss
news media. Media and Communication 11(3), 343–354.
Walczak, E. J. (2023). Postcodesior: An r package for uk geocoding. Journal of Open Source Software 8(84), 5334.
Waterson, J. (2021). Mirror owner to tell most journalists to permanently work from home. https://www.theguardian.com/
business/2021/mar/19/mirror-owner-tell-most-journalists-permanently-work-from-home-reach. The Guardian.
Accessed: 2025-01-13.
Wei, J., X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. V. Le, and D. Zhou (2022). Chain-of-thought
prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35, 24824–
24837.
Downloaded from https://www.cambridge.org/core. 12 Oct 2025 at 13:12:56, subject to the Cambridge Core terms of use.
20 Simona Bisiani et al.
Weiss, A. S. (2018). Journalism conundrum: Perceiving location and geographic space norms and values. Westminster Papers
in Communication and Culture 13(2), 46–60.
Yin, W., J. Hay, and D. Roth (2019). Benchmarking zero-shot text classification: Datasets, evaluation and entailment
approach. In K. Inui, J. Jiang, V. Ng, and X. Wan (Eds.), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp.
3914–3923. Association for Computational Linguistics.
Zhang, Z. and S. Bethard (2024). A survey on geocoding: algorithms and datasets for toponym resolution. Language
Resources and Evaluation 59, 1775–1796.
Zhao, X., K. Wang, and W. Peng (2024). An electoral approach to diversify LLM-based multi-agent collective decisionmaking. http://arxiv.org/abs/2410.15168. Accessed: 2025-01-13.
Zheng, L., W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. Xing, et al. (2023). Judging llm