A Path Toward the Use of Trail Users’ Tweets to Assess Effectiveness of the Environmental Stewardship Scheme: An Exploratory Analysis of the Pennine Way National Trail

Wilson, Tom; Lovelace, Robin; Evans, Andrew

doi:10.1007/s12061-016-9201-7

Cited by 9 publications

(11 citation statements)

References 32 publications

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“…We suggest that social media data can be enriched to better understand user experience, elicited through sentiment analysis of the textual metadata, and may provide a more robust dataset for CES assessments. Lexicon-based sentiment analysis is a natural language processing technique used to calculate the semantics, opinions or emotions of words or phrases from text (Wilson et al 2019). One form of analysis is polarity classification, which classifies text as either positive or negative and can be used to assess social media datasets (Koto and Adriani 2015).…”

Section: Introductionmentioning

confidence: 99%

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Enriching social media data allows a more robust representation of cultural ecosystem services

Fox¹,

Graham²,

Eigenbrod³

et al. 2021

Ecosystem Services

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Section: Introductionmentioning

confidence: 99%

“…Sentiment analysis for ES assessment has been broadly applied to social media datasets from Twitter (e.g. Becken et al 2017;Wilson et al 2019) and Instagram (e.g. Do 2019).…”

Section: Introductionmentioning

confidence: 99%

Enriching social media data allows a more robust representation of cultural ecosystem services

Fox¹,

Graham²,

Eigenbrod³

et al. 2021

Ecosystem Services

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“…Data from social media sites can be used in a range of methods. For example, using geolocated posts to assess the spatial variation in CES (Tieskens et al 2017); using content analysis to assess human-nature interactions depicted in photographs (Richards and Tunçer 2018); and using textual analysis to better understand opinions on CES (Becken et al 2017;Wilson et al 2019). Previous work in the field has examined the effectiveness of a variety of different social media platforms.…”

Section: Introductionmentioning

confidence: 99%

Geodiversity Supports Cultural Ecosystem Services: an Assessment Using Social Media

et al. 2022

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Geodiversity is under threat from both anthropogenic activities and environmental change which therefore requires active management in the form of geoconservation to minimise future damage. As research on the role of geodiversity on ecosystem service (ES) provision has been limited, there is a need to improve our understanding of which aspects are most important to providing ES to better inform approaches to its conservation. Here, we focus on the cultural ES of hiking in Wales, UK. Harnessing big data from the social media website Flickr, we used the locations of geotagged images of hiking and a range of spatial layers representing geodiversity, biodiversity and anthropogenic predictor variables in habitat suitability models. To gain a deeper understanding of the role of geodiversity in driving the distribution of this cultural service, we estimated the strength and nature of the relationship of each geodiversity, biodiversity and anthropogenic indicator with hiking. Our models show that three geodiversity (distance from coast, range in slope and range in elevation) and two anthropogenic (distance from greenspace access point and distance from road) variables were the most important drivers of hiking. Furthermore, we assessed the content of the images to understand which features of geodiversity people interact with while hiking. We found that people generally take images of geomorphological and hydrological features, such as mountains and lakes. Through understanding the geodiversity, biodiversity and anthropogenic drivers of hiking in Wales, as well as identifying the geodiversity features people interact with while hiking, this analysis can help to inform future geoconservation methods by focusing efforts on these important features.

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“…Foursquare, where users share information and opinions about locations (Glueck, 2018), has been used to spatially characterise cities based on the types of locations users visit (Zhou and Zhang, 2016). The posts on Twitter, known as 'tweets', have been used to track the effects of natural disasters (Chen et al, 2016;de Albuquerque et al, 2015;Middleton et al, 2014), measure user sentiments towards nature (Becken et al, 2017;Wilson et al, 2019) and determine the spatial distributions of outdoor recreation at small scales such as in urban park areas (Roberts et al, 2017;Zhou and Zhang, 2016).…”

Section: Crowdsourced Datamentioning

confidence: 99%

“…InVEST, a popular ES modelling tool, integrates Flickr photos in its recreation model (InVEST, 2017). At the same time, Twitter, a micro-blogging platform, has been used to gauge sentiments towards the environment (Wilson et al, 2019) and mobile exercise apps such as Strava have been drawn upon to examine cycling preferences in the urban environment (Griffin and Jiao, 2015;Sun et al, 2017). Mobile signal data has also been used to examine peoples' interactions with natural areas (Pei et al, 2014;Xiao et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Crowdsourced data and machine learning to measure cultural ecosystem services

Havinga

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Cultural ecosystem services (CES) are some of the most valuable contributions of ecosystems to human well-being. Nevertheless, these services are often underrepresented in ecosystem service assessments. Defining CES for the purposes of spatial quantification has been challenging because it has been difficult to spatially model CES. However, rapid increases in mobile network connectivity and the use of social media have generated huge amounts of crowdsourced data. This offers an opportunity to define and spatially quantify CES. We inventoried established CES conceptualisations and sources of crowdsourced data to propose a CES definition and typology for spatial quantification. Furthermore, we present the results of three spatial models employing crowdsourced data to measure CES on Texel, a coastal island in the Netherlands. Defining CES as information-flows best enables service quantification. A general typology of eight services is proposed. The spatial models produced distributions consistent with known areas of cultural importance on Texel. However, user representativeness and measurement uncertainties affect our results.Ethical considerations must also be taken into account. Still, crowdsourced data is a valuable source of information to define and model CES due to the level of detail available. This can encourage the representation of CES in ecosystem service assessments.

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A Path Toward the Use of Trail Users’ Tweets to Assess Effectiveness of the Environmental Stewardship Scheme: An Exploratory Analysis of the Pennine Way National Trail

Cited by 9 publications

References 32 publications

Enriching social media data allows a more robust representation of cultural ecosystem services

Enriching social media data allows a more robust representation of cultural ecosystem services

Geodiversity Supports Cultural Ecosystem Services: an Assessment Using Social Media

Crowdsourced data and machine learning to measure cultural ecosystem services

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