A Forecast Model of Tourism Demand Driven by Social Network Data

Peng, Tao; Chen, Jian; Wang, Chenjie; Cao, Yanshi

doi:10.1109/access.2021.3102616

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…Regarding analytical data tools in tourism, the use of Big Data [60,[67][68][69] and predictive analysis [70][71][72][73] has become fundamental. The ability to analyze large volumes of realtime data, ranging from booking pattern detection to integrated analysis of social media comments, enables a deeper understanding of tourism market trends.…”

Section: Conceptual Framework and State Of The Art: Geo-dashboardsmentioning

confidence: 99%

Toward Establishing a Tourism Data Space: Innovative Geo-Dashboard Development for Tourism Research and Management

Ordóñez-Martínez,

Seguí-Pons,

Ruiz-Pérez

2024

Smart Cities

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The data sharing strategy involves understanding the challenges and problems that can be solved through the collaboration of different entities sharing their data. The implementation of a data space in Mallorca is based on understanding the available data and identifying the problems that can be solved using them. The use of data through data spaces will contribute to the transformation of destinations into smart tourism destinations. Smart tourism destinations are considered as smart cities in which the tourism industry offers a new layer of complexity in which technologies, digitalization, and intelligence are powered by data. This study analyzes four scenarios in which geo-dashboards are developed: flood exposure of tourist accommodation, land-cover changes, human pressure, and tourist uses in urban areas. The results of applying the geo-dashboards to these different scenarios provide tourists and destination managers with valuable information for decision-making, highlighting the utility of this type of tool, and laying the foundations for a future tourism data space in Mallorca.

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Section: Conceptual Framework and State Of The Art: Geo-dashboardsmentioning

confidence: 99%

Toward Establishing a Tourism Data Space: Innovative Geo-Dashboard Development for Tourism Research and Management

Ordóñez-Martínez,

Seguí-Pons,

Ruiz-Pérez

2024

Smart Cities

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“…Park et al [15] apply the news as data for predicting tourist arrivals in Hong Kong. Peng et al [38] implemented social network data, sentiment analysis, and Gradient Boosting Regression Trees to forecast Huang Shan tourism demand, which has always resulted in good forecasting performance. Fronzetti et al [39] employed Factor Augmented Autoregressive and Bridge models with social network and semantic variables which have the highest performance than other algorithms based on GoogleTrend data.…”

Section: Tourism Demand Forecasting With Online Datamentioning

confidence: 99%

Deep learning approach and topic modelling for forecasting tourist arrivals

Laaroussi

Guerouate

Sbihi

2023

Int. j. electr. comput. eng. syst. (Online)

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Online review data attracts the attention of researchers and practitioners in various fields, but its application in tourism is still limited. The social media data can finely reflect tourist arrivals forecasting. Accurate prediction of tourist arrivals is essential for tourism decision-makers. Although current studies have exploited deep learning and internet data (especially search engine data) to anticipate tourism demand more precisely, few have examined the viability of using social media data and deep learning algorithms to predict tourism demand. This study aims to find the key topics extracted from online reviews and integrate them into the deep learning model to forecast tourism demand. We present a novel forecasting model based on TripAdvisor reviews. Latent topics and their associated keywords are captured from reviews through Latent Dirichlet Allocation (LDA), These generated features are then employed as an additional feature into the deep learning (DL) algorithm to forecast the monthly tourist arrivals to Hong Kong from USA. We used machine learning models, artificial neural networks (ANNs), support vector regression (SVR), and random forest (RF) as benchmark models. The empirical results show that the proposed forecasting model is more accurate than other models, which rely only on historical data. Furthermore, our findings indicate that integration of the topics extracted from social media reviews can enhance the prediction.

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“…The vast development of information and technology has also increase social interaction, as it can be an alternative to look for advice or share information, hence affecting consumer behavior (Filieri et al, 2021;Chan & Tung, 2023). The internet and IT has been rapidly utilized, and it is estimated that out of a global population of 7.4 billion, 2.9 billion are active internet users and this will continue to grow (Peng et al, 2021). As stated by Kotler et al (2019), the digital era has given birth to a set of tools that can build relationships with consumers, ranging from websites, advertisements, and online videos that can be accessed on mobile devices via social media.…”

Section: Introductionmentioning

confidence: 99%

Consumer Behaviour in Online Travel: How Does Continuous Purchase Behaviour Formed in Digital Age?

Pratminingsih,

Zulganef,

Purwaningdyah

et al. 2024

MABIS

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Research aims: This study aims to understand the role of perceived ease of use, information service function, consumer expectation, and user satisfaction in determining continuous purchase behavior in online travel application users, while considers consumer experience as a moderating variable.Design/Methodology/Approach: This study is carried out using quantitative approach. The data is collected by distributing online questionnaire to users of e-travel application in West Java, Indonesia chosen using purposive sampling. The data is processed using Structural Equation Modeling with Partial Least Square (SEM PLS).Research findings: The results indicate that perceived ease of use, information service function, consumer expectation, and user satisfaction can explain customers’ continuous purchase behavior. In addition, consumer experience can moderate the relationship between user satisfaction and continuous purchase behavior, by which higher experience strengthens the relationship. Theoretical Contribution/Originality: This study contributes to enrich the understanding of how user satisfaction affects continuous purchase behavior by looking at the variables of perceived ease of use, information service function, and user expectation.Practitioners/Policy Implications: This study can be of interest to service providers, especially e-travel application, to provide a good, easy to understand services, as well as information and comfort for its users to remain loyal in using the application.Research Limitations/Implications: This study is only limited to one object, namely users of e-travel application. Future research can use different objects and consider additional variables such as review credibility and perceived enjoyment in determining continuous purchase behavior.

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A Forecast Model of Tourism Demand Driven by Social Network Data

Cited by 14 publications

References 19 publications

Toward Establishing a Tourism Data Space: Innovative Geo-Dashboard Development for Tourism Research and Management

Toward Establishing a Tourism Data Space: Innovative Geo-Dashboard Development for Tourism Research and Management

Deep learning approach and topic modelling for forecasting tourist arrivals

Consumer Behaviour in Online Travel: How Does Continuous Purchase Behaviour Formed in Digital Age?

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