English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A better understanding of the behavior of tourists is strategic for improvingservices in the competitive and important economic segment of global tourism.Critical studies in the literature often explore the issue using traditionaldata, such as questionnaires or interviews. Traditional approaches provideprecious information; however, they impose challenges to obtaining large-scaledata, making it hard to study worldwide patterns. Location-based socialnetworks (LBSNs) can potentially mitigate such issues due to the relatively lowcost of acquiring large amounts of behavioral data. Nevertheless, before usingsuch data for studying tourists' behavior, it is necessary to verify whetherthe information adequately reveals the behavior measured with traditional data-- considered the ground truth. Thus, the present work investigates in whichcountries the global tourism network measured with an LBSN agreeably reflectsthe behavior estimated by the World Tourism Organization using traditionalmethods. Although we could find exceptions, the results suggest that, for mostcountries, LBSN data can satisfactorily represent the behavior studied. We havean indication that, in countries with high correlations between resultsobtained from both datasets, LBSN data can be used in research regarding themobility of the tourists in the studied context.

Comparing Global Tourism Flows Measured by Official Census and Social Sensing