Finding patterns in urban tourist behaviour: a social network analysis approach based on TripAdvisor reviews

Zee, Egbert van der; Bertocchi, Dario

doi:10.1007/s40558-018-0128-5

Cited by 49 publications

(19 citation statements)

References 45 publications

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“…The information sources used by travelers represent information search strategy, inherently characterizing the information environment (Fodness and Murray 1998). Evidence has been found for systematic relationships between information search strategies and individual and situational characteristics of the travelers (David-Negre et al 2018; Iinuma et al 2018; Park, Wang, and Fesenmaier 2011; Van der Zee and Bertocchi 2018). More pertinently, several researchers explore the link between information search behaviors and travel outcomes, including trip expenditure (Kambele, Li, and Zhou 2015).…”

Section: Determinant Factors Of Tourism Expensesmentioning

confidence: 99%

Determinant Factors of Tourist Expenses

Park

Woo

Nicolau

2019

Journal of Travel Research

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The objective of this article is to analyze travel expenses across and within types. The empirical application examines the determinant factors of total expenses, controlling for potential endogeneity, and relies on quantile regression to analyze the effects of information search behavior on the distribution of total expenses as well as accommodation, shopping, food and beverages, and local transportation expenses. The role of information sources in predicting travel spending behaviors is a new dimension in the literature on expenses, and a sample of 48,113 travelers has led to the detection of effects of variables with relevant managerial implications (e.g., while official information centers show positive impacts at the upper levels of accommodation expenses, they present null effects at the highest levels of shopping expenses) as well as theoretical implications (special attention should be drawn to the variable length of stay, which, after being controlled by endogeneity, completely loses its significance).

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Section: Determinant Factors Of Tourism Expensesmentioning

confidence: 99%

Determinant Factors of Tourist Expenses

Park

Woo

Nicolau

2019

Journal of Travel Research

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“…Some works have shown the general reliability of these data for comprehensive studies, by checking the correlation between check-ins on social media and standard cellphone and survey data [10]. Research that uses social media data in fields such as recommender systems [11], spatial analysis [12][13][14][15] and human behaviour modelling [16] has become increasingly common.…”

Section: Related Work 21 Social Media User Profilingmentioning

confidence: 99%

Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities

Orama¹,

Borrás²,

Moreno³

2021

Applied Sciences

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Tourists who visit a city for the first time may find it difficult to decide on places to visit, as the amount of information in the Web about cultural and leisure activities may be large. Recommender systems address this problem by suggesting the points of interest that fit better with the user’s preferences. This paper presents a novel recommender system that leverages tweets to build user profiles, taking into account not only their personal preferences but also their travel habits. Association rules, which are mined from the previous visits of users documented on Twitter, are used to make the final recommendations of places to visit. The system has been applied to data of the city of Barcelona, and the results show that the use of the social media-based clustering procedure increases its performance according to several relevant metrics.

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“…When researchers conduct DI studies based on shared online travel experiences, OTRs are regarded as a form of electronic word-of-mouth communication (eWOM) (Marine-Roig 2017 ). Although online reviews may be seen as unsolicited and unbiased online information that reflects the realistic tourist perception of the destination (Marine-Roig 2017 ), the OTR content given by different tourist segments has different focuses (Van der Zee and Bertocchi 2018 ). Nowadays, the application of OTRs in tourism research has received increasing attention, and researchers usually collect data from a single Western OTR platform, especially TripAdvisor, Yelp, or Expedia (Xiang et al 2017 ).…”

Section: Social Media Analytics On Online Travel Review Platformsmentioning

confidence: 99%

Comparing online travel review platforms as destination image information agents

Guo

Pesonen

Komppula

2021

Inf Technol Tourism

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Online travel reviews have been extensively used as an important data source in tourism research. Typically, data for online travel review research is collected only from one platform. However, drawing definite conclusions based on single platform analyses may thus produce biases and lead to erroneous conclusions and decisions. Therefore, this research verifies whether or not there are discrepancies and commonalities between different travel review platforms. In this study, five native Chinese travel review platforms were selected: Ctrip; Qyer; Mafengwo; Tuniu; and Qunar. Using a mixed content analysis method, the destination image of Finland was extracted from 10,197 travel reviews in Simplified Chinese as the destination image is a popular topic in online review research. Results show Finland’s destination image representation varies between Chinese travel review platforms. This discrepancy is especially prominent in the dimension of functional and mixed functional-psychological destination attributes. Significant theoretical contributions and managerial implications for the analysis of online travel reviews and destination image research are discussed.

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Finding patterns in urban tourist behaviour: a social network analysis approach based on TripAdvisor reviews

Cited by 49 publications

References 45 publications

Determinant Factors of Tourist Expenses

Determinant Factors of Tourist Expenses

Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities

Comparing online travel review platforms as destination image information agents

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