Beyond big data: Social media challenges and opportunities for understanding social perception of energy

Li, Ruopu; Crowe, Jessica; Leifer, David; Zou, Lei; Schoof, J. T.

doi:10.1016/j.erss.2019.101217

Cited by 47 publications

(47 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…other fields in humanities and social sciences. Mining the semantic information related to tourism destinations, cities, energy and so on is the main focus of current research which includes studies of TDP [5,38], tourist emotion [39], intention perception [40], areas of interest [41], travel trajectory [42], energy development [43] and so on. Moreover, due to the multiscale characteristics of geographic datasets in both the spatial and temporal dimensions, it is necessary to mine and analyze the semantic knowledge at multi-spatiotemporal scales [44].…”

Section: Plos Onementioning

confidence: 99%

Measuring multi-spatiotemporal scale tourist destination popularity based on text granular computing

Chi¹,

Li²,

Zhao³

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

User-generated content (UGC) is an important data source for tourism GIScience research. However, no effective approach exists for identifying hidden spatiotemporal patterns within multi-scale unstructured UGC. Therefore, we developed an algorithm to measure the tourist destination popularity (TDP) based on a multi-spatiotemporal text granular computing model, called TDPMTGC. To accurately granulate the spatial and temporal information of tourism text, tourism text data granules are used to represent landscape objects. These granules are unified objects that possess multiple attributes, such as spatial and temporal dimensions. The multi-spatiotemporal scales are characterized by the multi-hierarchical structure of granular computing, and transformations of granular layers and data granule size are achieved by scale selection in the spatial and temporal dimensions. Therefore, all scales between the spatial and temporal dimension are related, which allows for the comparability of the data granules of all spatial-spatial, temporal-temporal and spatial-temporal layers. This approach achieves a quantitative description and comparison of the popularity value of granules between adjacent scales and cross-scales. Therefore, the TDP with multispatiotemporal scales can be deduced and calculated in a systematic framework. We first introduce the conceptual framework of TDPMTGC to construct a quantitative measurement model of TDP at multi-spatiotemporal scales. Then, we present a dataset construction approach to support multi-spatiotemporal scale granular reorganization. Finally, TDPMTGC is derived to describe both the TDP at a single spatial or temporal scale and the patterns and processes of the TDP at multi-spatiotemporal scales. A case study from Jiuzhaigou shows that the TDP derived using TDPMTGC is consistent with the conclusions of existing studies. More importantly, TDPMTGC provides additional detailed characteristics, such as the contributions of different scenic spots in a tourist route or scenic area, the monthly anomalies and daily contributions of TDP in a specific year, the distinct weakening of tourist route scale in tourist cognition, and the daily variations of TDP during in-season and off-season times. This is the first time that a granular computing model has been introduced to tourism GIScience that provides a feasible scheme for reorganizing large-scale unstructured text

show abstract

Section: Plos Onementioning

confidence: 99%

Measuring multi-spatiotemporal scale tourist destination popularity based on text granular computing

Chi¹,

Li²,

Zhao³

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…However, at the same time, we have never been more connected and willing to share our thoughts and opinions in the comments section of online newspapers or in our social networks. In this context, there are more and more examples of research that extract public opinion information from the net and social media on issues such as hospitality and tourism management [9], urban mobility [10], health [11], or the social perception of energy [12], among others. In addition, new methods and techniques are being developed in order to take advantage of the enormous amount of information available from a wide range of online sources [13].…”

Section: Introductionmentioning

confidence: 99%

Treating Nonresponse in Probability-Based Online Panels through Calibration: Empirical Evidence from a Survey of Political Decision-Making Procedures

2020

View full text Add to dashboard Cite

The use of probability-based panels that collect data via online or mixed-mode surveys has increased in the last few years as an answer to the growing concern with the quality of the data obtained with traditional survey modes. However, in order to adequately represent the general population, these tools must address the same sources of bias that affect other survey-based designs: namely under coverage and non-response. In this work, we test several approaches to produce calibration estimators that are suitable for survey data affected by non response where auxiliary information exists at both the panel level and the population level. The first approach adjusts the results obtained in the cross-sectional survey to the population totals, while, in the second, the weights are the result of two-step process where different adjusts on the sample, panel, and population are done. A simulation on the properties of these estimators is performed. In light of theory and simulation results, we conclude that weighting by calibration is an effective technique for the treatment of non-response bias when the response mechanism is missing at random. These techniques have also been applied to real data from the survey Andalusian Citizen Preferences for Political Decision-Making Procedures.

show abstract

“…En la actualidad, multitud de individuos acuden a las redes sociales públicas como Twitter tanto para obtener información como para discutir acerca de temas relacionados con la ciencia . En esta dirección, se han proporcionado evidencias de que el análisis de las redes sociales es un complemento útil a las encuestas tradicionales de percepción social de la ciencia (Ceron, Curini, Iacus, & Porro, 2014;R. Li, Crowe, Leifer, Zou, & Schoof, 2019;Murphy et al, 2013), en el sentido de que admiten una mirada más continua a las opiniones, actitudes y comportamientos (Murphy et al, 2014) al ofrecer información contextual valiosa -cuantitativa y cualitativa-.…”

Section: Twitter Como Alternativa a Las Encuestas I) Hacia La Particiunclassified

“…Li, Crowe, Leifer, Zou, & Schoof, 2019;Murphy et al, 2013), en el sentido de que admiten una mirada más continua a las opiniones, actitudes y comportamientos (Murphy et al, 2014) al ofrecer información contextual valiosa -cuantitativa y cualitativa-. Esto proporciona un nuevo prisma desde el cual pueden observarse y tratar de comprenderse las actitudes y la percepción del público desde una óptica no tradicional (Li, 2019). Algunos estudios empíricos preliminares, incluso, sugieren que las redes sociales favorecen la confianza pública en la ciencia (Huber, Barnidge, Gil de Zúñiga, & Liu, 2019).…”

Section: Twitter Como Alternativa a Las Encuestas I) Hacia La Particiunclassified

“…Desde la academia se advierte de que es necesaria una mejor comprensión acerca de cómo afectan los nuevos entornos virtuales a esta práctica , que a su vez brindan la posibilidad de investigar de cerca los debates públicos en torno a temas científicos prestando atención a nuevas voces y contextos diversos (Kapoor et al, 2018;Shan et al, 2014), y en los que el acceso a los contenidos, en muchas ocasiones, se produce sin mediadores. Es por ello que los análisis en redes sociales pueden constituir un complemento útil a las encuestas clásicas de percepción social de la ciencia (Li et al, 2019).…”

Section: Codificador Humano Word Categorizationunclassified

See 1 more Smart Citation

Percepción social de la ciencia y participación digital: impacto de la comunicación científica en Twitter

Navarro¹

View full text Add to dashboard Cite

show abstract

Beyond big data: Social media challenges and opportunities for understanding social perception of energy

Cited by 47 publications

References 26 publications

Measuring multi-spatiotemporal scale tourist destination popularity based on text granular computing

Measuring multi-spatiotemporal scale tourist destination popularity based on text granular computing

Treating Nonresponse in Probability-Based Online Panels through Calibration: Empirical Evidence from a Survey of Political Decision-Making Procedures

Percepción social de la ciencia y participación digital: impacto de la comunicación científica en Twitter

Contact Info

Product

Resources

About