Cite this as: Tenzer, M. 2022 Tweets in the Peak: Twitter Analysis - the impact of Covid-19 on cultural landscapes, Internet Archaeology 59. https://doi.org/10.11141/ia.59.6
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.
Corresponding author: Martina Tenzer
martina.tenzer@york.ac.uk
University of York
Figure 1: Study area: Peak District National Park (map created by M. Tenzer, basemap © OpenStreetMap contributors)
Figure 2: Network graph visualisation of word associations based on frequently used words, such as 'walk', 'see', 'view', 'honeypot', 'landscape', 'place', as well as words of special interest regarding heritage management, such as 'heritage', 'history', and 'monument'. The colours represent the three years. The visualisation represents statistical associations of the words
Figure 3: Word cloud of most frequently used words across the study period. Disclaimer: The words and their position are not geospatially located within the Peak District National Park, but rather randomly located within the boundary to visualise the summary and frequency of the most used words in tweets related to the area within these boundaries
Figure 4: Sentiment analysis with VADER sentiment algorithm based solely on emojis. The emoji score is normalised for the sentiment categories of the respective years. Note: the category 'Neutral' has been excluded from the visualisation, as this would skew the plot, as this category combined all the tweets with no emojis, neutral emojis and unknown emojis
Figure 5: Cloud of most frequently used emojis across the study period. Disclaimer: the emojis and their position are not geospatially located within the Peak District National Park, but rather randomly located within the boundary to visualise the summary and frequency of the most used emojis in tweets related to the area within these boundaries
Figure 6: Sentiment analysis with VADER sentiment algorithm. The score is normalised for the sentiment categories of the respective years
Figure 7: Confusion matrix comparing manual and automated sentiment analysis for the year 2020 based on emojis. Bottom left, middle and top right fields show the number of True-Negative, True-Neutral and True-Positive results of the recognition algorithm, respectively. Off-diagonal fields show false matches. Notably the top left field with 119 False-Positives due to misinterpretation of sarcasm in tweets.
Figure 8: Confusion matrix comparing manual and automated sentiment analysis for the year 2019 based on emojis. Bottom left, middle and top right fields show the number of True-Negative, True-Neutral and True-Positive results of the recognition algorithm, respectively. Off-diagonal fields show false matches.
Figure 9: Confusion matrix comparing manual and automated sentiment analysis for the year 2021 based on emojis. Bottom left, middle and top right fields show the number of True-Negative, True-Neutral and True-Positive results of the recognition algorithm, respectively. Off-diagonal fields show false matches.
Figure 10: Building a corpus of locations in GIS gradually in steps, adding levels of information from various sources, such as rivers, place names, points of interest from Ordnance Survey data, or historic information from Historic England data (Image: M. Tenzer)
Figure 11: Confusion matrix, comparing manual and automated place recognition. Bottom left and top right quadrants show the number of True-Negative and True-Positive results of the recognition algorithm, respectively. Top left and bottom right show False-Positive and False-Negative matches, respectively
Figure 12: QGIS2Web OpenLayers map showing point data and heatmaps of locations mentioned in tweet texts of the Spring bank holiday weekends 2019 to 2021. Base maps used in this project provide the background for orientation and navigation of the map. For map use, please zoom in for details, and switch layers on and off in the top right corner menu
Figure 13: Heatmap visualisation of the years 2019 to 2021 showing the shift of locations mentioned in tweets visualised in QGIS (map created by M. Tenzer, basemap map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL)
Figure 14: Another option for visualising hot spot locations mentioned in tweets for the years 2019-2021 in one 2D map: point size based on weight (frequency) visualised in QGIS (map created by M. Tenzer, basemap map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL)
Table 1: Top 40 of the most frequently used hashtags across the study period
Table 2: Top 30 of the most frequently used words across the study period
Table 3: Top 20 of the most frequently used emojis across the study period
Table 4: User location of tweets about the Peak District National Park with the 25 most frequently used location descriptions
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