Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria

Ulrich Gunter, Irem Önder and Stefan Gindl
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Ulrich Gunter: MODUL University Vienna, Austria
Irem Önder: MODUL University Vienna, Austria
Stefan Gindl: MODUL University Vienna, Austria

Tourism Economics, 2019, vol. 25, issue 3, 375-401

Abstract: Using data for the period 2010M06–2017M02, this study investigates the possibility of predicting total tourist arrivals to four Austrian cities (Graz, Innsbruck, Salzburg, and Vienna) from LIKES of posts on the Facebook pages of the destination management organizations of these cities. Google Trends data are also incorporated in investigating whether forecast models with LIKES and/or with Google Trends deliver more accurate forecasts. To capture the dynamics in the data, the autoregressive distributed lag (ADL) model class is employed. Taking into account the daily frequency of the original LIKES data, the mixed data sampling (MIDAS) model class is employed as well. While time-series benchmarks from the naive, error–trend–seasonal, and autoregressive moving average model classes perform best for Graz and Innsbruck across forecast horizons and forecast accuracy measures, ADL models incorporating only LIKES or both LIKES and Google Trends generally outperform their competitors for Salzburg. For Vienna, the MIDAS model including both LIKES and Google Trends produces the smallest forecast accuracy measure values for most forecast horizons.

Keywords: ADL; big data; Facebook LIKES; Google Trends; MIDAS; tourism demand forecasting (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:toueco:v:25:y:2019:i:3:p:375-401

DOI: 10.1177/1354816618793765

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