TY - GEN
T1 - Improving tourism prediction models using climate and social media data
T2 - 12th International AAAI Conference on Web and Social Media, ICWSM 2018
AU - Khatibi, Amir
AU - Belem, Fabiano
AU - Silva, Ana P.
AU - Shasha, Dennis
AU - Almeida, Jussara M.
AU - Gonçalves, Marcos A.
N1 - Funding Information:
This work is supported by CNPq, CAPES, and Fapemig and INRIA (for Shasha).
Publisher Copyright:
Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2018
Y1 - 2018
N2 - Accurate predictions about future events is essential in many areas, one of them being the Tourism Industry. Usually, countries and cities invest a huge amount of money in planning and preparation in order to welcome (and profit from) tourists. An accurate prediction of the number of visits in the following days or months could help both the economy and tourists. Prior studies in this domain explore forecasting for a whole country rather than for fine-grained areas within a country (e.g., specific touristic attractions). In this work, we suggest that accessible data from online social networks and travel websites, in addition to climate data, can be used to support the inference of visitation count for many touristic attractions. To test our hypothesis we analyze visitation, climate and social media data in more than 70 National Parks in U.S during the last 3 years. The experimental results reveal a high correlation between social media data and tourism demands; in fact, in over 80% of the parks, social media reviews and visitation counts are correlated by more than 50%. Moreover, we assess the effectiveness of employing various prediction techniques, finding that even a simple linear regression model, when fed with social media and climate data as input features, can attain a prediction accuracy of over 80% while a more robust algorithm, such as Support Vector Regression, reaches up to 94% accuracy.
AB - Accurate predictions about future events is essential in many areas, one of them being the Tourism Industry. Usually, countries and cities invest a huge amount of money in planning and preparation in order to welcome (and profit from) tourists. An accurate prediction of the number of visits in the following days or months could help both the economy and tourists. Prior studies in this domain explore forecasting for a whole country rather than for fine-grained areas within a country (e.g., specific touristic attractions). In this work, we suggest that accessible data from online social networks and travel websites, in addition to climate data, can be used to support the inference of visitation count for many touristic attractions. To test our hypothesis we analyze visitation, climate and social media data in more than 70 National Parks in U.S during the last 3 years. The experimental results reveal a high correlation between social media data and tourism demands; in fact, in over 80% of the parks, social media reviews and visitation counts are correlated by more than 50%. Moreover, we assess the effectiveness of employing various prediction techniques, finding that even a simple linear regression model, when fed with social media and climate data as input features, can attain a prediction accuracy of over 80% while a more robust algorithm, such as Support Vector Regression, reaches up to 94% accuracy.
KW - Climate data
KW - Machine learning
KW - Social media
KW - Time-series analysis
KW - Tourism demand prediction
UR - http://www.scopus.com/inward/record.url?scp=85050625502&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050625502&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85050625502
T3 - 12th International AAAI Conference on Web and Social Media, ICWSM 2018
SP - 636
EP - 639
BT - 12th International AAAI Conference on Web and Social Media, ICWSM 2018
PB - AAAI press
Y2 - 25 June 2018 through 28 June 2018
ER -