The Covid-19 pandemic had an unprecedented impact on society, with restrictions on socialising and movement during the three lockdown periods between March 2020 and March 2021 (Baker et al. 2021; Institute for Government Analysis 2021). Easily accessible locations offering the typical qualities of tourist destinations moved into the focus of day visitors in periods when restriction eased. The Peak District National Park (PDNP), a cultural landscape comprising historical places, natural beauty spots, and 'chocolate box' villages, offered a way of satisfying the urge to escape to the countryside. The impact was also felt in the heritage sector, with a noticeable change in visitor behaviour and the relationship between park residents and day tourists (Jones and McGinlay 2020; Sofaer et al. 2021). In order to understand societal change, social media research gives a unique insight into the sentiments, actions, and controversies associated with tourism, Covid-19, and nature conservation. In particular, the open and public nature of Twitter data offers itself for the analysis of large datasets based on specific search queries at specific time periods. For this research, tweets from the PDNP for three weekends in 2019 to 2021 with different restriction levels were collected. Using R and Python, automated processes allow the time-efficient analysis of qualitative information. This project has extended the standard procedures of social media analysis, such as keyword search and sentiment analysis by an emoji analysis and location entity recognition, focusing specifically on cultural and natural heritage. Using Twitter data in a time-efficient process and creating visually appealing outputs may foster an appreciation of the park's resources and positively influence the behaviour of visitors and residents. Going forward, improving the relationship between people and places will provide background for the management of cultural landscapes and help tackle environmental issues, such as peat erosion resulting from a large influx of walkers, address the climate change emergency, and help ease the controversial relationship between a living and working landscape and tourism.

The historic environment—comprising a palimpsest of landscapes, buildings and objects—carries meaning and plays a crucial role in giving people a sense of place, identity and belonging. It represents a repository of ever-accumulating collective and individually held values—shared perceptions, experiences, life histories, beliefs and traditions. These social or private values are mostly ascribed by people to familiar places within this environment based on the ontological security which this everyday heritage provides. However, these values are notoriously hard to capture and categorize. This makes it difficult to incorporate them into heritage-management strategies, which typically rely on objective, fact-based datasets. In this paper, we present a new methodology to capture those elusive values, by combining Topic Modelling with the principles of Grounded Theory. Results show that our novel approach is viable and replicable and that these important values can be effectively and meaningfully integrated, thus creating more inclusive approaches to heritage management than exist currently.

“Artificial Intelligence” (AI) is not a recent development. However, with increasing computational capabilities, AI has developed into Natural Language Processing and Machine Learning, technologies particularly good at detecting correlations and patterns, and categorising, predicting, or extracting information. Within archaeology, AI can process “big data” accumulated over decades of research and deposited in archives. By combining these capabilities, AI offers new insights and exciting opportunities to create knowledge from archaeological archives for contemporary and future research. However, ethical implications and human costs are not yet fully understood. Therefore, we question whether AI in archaeology is a blessing or a curse?