ΑΙhub.org
 

History playground – finding patterns in historical newspapers


by
13 January 2020



share this:

Ever fancied finding out more about historical trends? Well, thanks to researchers at the University of Bristol, and their History Playground tool, anyone can analyse the content from a collection of historical British and American newspapers.

Macroscopic patterns of continuity and change over the course of centuries can be detected through the analysis of time series extracted from massive textual corpora. Similar data-driven approaches have already revolutionised the natural sciences. It is widely believed that there is similar potential for the humanities and social sciences. As such, new interactive tools are required to discover and extract macroscopic patterns from these vast quantities of data.

History Playground enables users to search for small sequences of words and retrieve their relative frequencies over the course of history. The tool makes use of scalable algorithms to first extract trends from textual corpora, before making them available for real-time search and discovery, presenting users with an interface to explore the data.

At present there are two large sets of text available:

Find out how to start using the History Playground by watching this short video:

Watch a further introduction to the project here:

History Playground uses the concept of n-grams, defined as short sequences of words. It is these n-grams that users search for when they use the tool. N-gram models are also widely used in the fields of natural language processing, probability, communication theory and data compression.

The team hope that in the long term, as more large textual datasets are released and additional feedback from the community helps to improve the Playground, they will be able to incorporate more varied and interesting corpora into the tool. In addition they are continuing to develop methods of analysis and additional views and visualisations. The tool also has the potential to incorporate text in languages other than English. For looking at more contemporary sources of data (for example, social media) the time resolution can be adjusted to study daily or even hourly changes.

This work is part of the ERC ThinkBIG project, Principal Investigator Nello Cristianini, University of Bristol.

Nello Cristianini is a Professor of Artificial Intelligence at the University of Bristol. His research interests include data science, artificial intelligence, machine learning, and applications to computational social sciences, digital humanities and news content analysis.

 

 

Read the full research articles on this topic:




Nello Cristianini is a Professor of Artificial Intelligence at the University of Bristol.
Nello Cristianini is a Professor of Artificial Intelligence at the University of Bristol.




            AIhub is supported by:



Related posts :



Deploying agentic AI: what worked, what broke, and what we learned

  15 Sep 2025
AI scientist and researcher Francis Osei investigates what happens when Agentic AI systems are used in real projects, where trust and reproducibility are not optional.

Memory traces in reinforcement learning

  12 Sep 2025
Onno writes about work presented at ICML 2025, introducing an alternative memory framework.

Apertus: a fully open, transparent, multilingual language model

  11 Sep 2025
EPFL, ETH Zurich and the Swiss National Supercomputing Centre (CSCS) released Apertus today, Switzerland’s first large-scale, open, multilingual language model.

Interview with Yezi Liu: Trustworthy and efficient machine learning

  10 Sep 2025
Read the latest interview in our series featuring the AAAI/SIGAI Doctoral Consortium participants.

Advanced AI models are not always better than simple ones

  09 Sep 2025
Researchers have developed Systema, a new tool to evaluate how well AI models work when predicting the effects of genetic perturbations.

The Machine Ethics podcast: Autonomy AI with Adir Ben-Yehuda

This episode Adir and Ben chat about AI automation for frontend web development, where human-machine interface could be going, allowing an LLM to optimism itself, job displacement, vibe coding and more.

Using generative AI, researchers design compounds that can kill drug-resistant bacteria

  05 Sep 2025
The team used two different AI approaches to design novel antibiotics, including one that showed promise against MRSA.

#IJCAI2025 distinguished paper: Combining MORL with restraining bolts to learn normative behaviour

and   04 Sep 2025
The authors introduce a framework for guiding reinforcement learning agents to comply with social, legal, and ethical norms.



 

AIhub is supported by:






 












©2025.05 - Association for the Understanding of Artificial Intelligence