The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
An Exploratory Analysis of Geotagged Photos From Instagram for Residents of and Visitors to Vienna
Abstract
This study uses geotagged photos from Instagram to identify differences between the popular places in Vienna for residents and visitors. Moreover, we explore whether geotagged data can be useful in determining tourism demand in Vienna. The spatial analysis of 627,632 geotagged photos reveals the top-50 locations in Vienna for all-, local-, and visiting-Instagram users based on three popularity indicators (numbers of likes, comments, and photos). The results show that the top locations unique to local users are closely linked to activities residents usually pursue or location types they usually visit at their place of dwelling. In using geotagged photos to predict actual tourist arrivals to Vienna, we conclude that only the popularity indicators number of likes and number of comments based on the location ID “Vienna, Austria” for visitors to Vienna should be used and not the number of photos, since this indicator does not automatically generate engagement.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In big social media datasets, posting 323 duration and frequency serve as reliable proxies for differentiating residents from visitors 324 when the geolocation data is incomplete.”
From Drivers of engagement with influencer posts about tourism destinations on Instagram: the role of number of followers, destination connection and content sentiment (2026) · cites this paper as Gunter and Önder, 2021
Citations
Cites
A Big Data Analytics Method for Tourist Behaviour Analysis
Miah, S. J., Vu, H. Q., Gammack, J., & McGrath, M. (2017) · Information & Management · doi:10.1016/j.im.2016.11.011 · Cited by 393
Research works printed but not linked: 53
- 10.2307/1911894 · DOI
- 10.1016/j.jeconom.2010.01.004 · DOI
- 10.20955/r.92.521-36 · DOI
- 10.1016/0167-6245(94)90037-X · DOI
- Askitas N., Zimmermann K. F. (2009). Google econometrics and unemployment forecasting (DIW Berlin Discussion Paper No. 4201). http://ftp.iza.org/dp4201.pdf · DOI
- 10.1016/j.tourman.2014.07.014 · DOI
- Chatfield C., 2001, Time series forecasting
- 10.2307/41703503 · DOI · Paper page
- 10.1145/1631272.1631415 · DOI
- 10.1016/j.tourman.2016.06.013 · DOI · Paper page
- 10.1198/073500108000000015 · DOI
- 10.1145/1810617.1810626 · DOI
- 10.1177/1536867X1301300202 · DOI
- European Cities Marketing & MODUL University Vienna. (2018). The European Cities Marketing benchmarking report, 14th official edition 2017-2018. https://www.europeancitiesmarketing.com/european-cities-marketing-benchmarking-report-2018-shows-the-continuous-growth-of-european-city-tourism/
- EViews, 2017, EViews 10 user’s guide
- 10.1016/j.tele.2010.03.003 · DOI
- Frechtling D. C., 2001, Forecasting tourism demand: Methods and strategies
- 10.1016/j.apgeog.2015.08.002 · DOI
- 10.2307/2986645 · DOI
- 10.1016/j.jfineco.2004.03.008 · DOI
- 10.1016/j.jeconom.2005.01.004 · DOI
- 10.1109/MPRV.2008.71 · DOI
- 10.2307/1912791 · DOI · Paper page
- 10.1016/j.tourman.2014.06.017 · DOI
- 10.1016/j.annals.2016.10.007 · DOI
- 10.1177/1354816618793765 · DOI
- 10.1016/j.compenvurbsys.2015.09.001 · DOI
- 10.18637/jss.v027.i03 · DOI
- 10.1016/j.neucom.2012.02.049 · DOI
- 10.3138/carto.48.2.1839 · DOI
- 10.1109/IV.2010.94 · DOI
- 10.1007/978-3-540-27752-1 · DOI
- 10.1109/IE.2010.34 · DOI
- Mas-Colell A., 1995, Microeconomic theory
- 10.1111/j.2517-6161.1948.tb00012.x · DOI
- 10.1016/j.tmp.2016.11.002 · DOI
- 10.3727/108354216X14559233984773 · DOI
- 10.1177/0047287514563985 · DOI
- 10.1093/acprof:oso/9780198509882.001.0001 · DOI
- Pisati M., 2010, Stata Technical Bulletin, 60, 21
- 10.1145/1645953.1646211 · DOI
- 10.1016/0169-2070(94)90019-1 · DOI
- 10.1007/978-3-319-44263-1_2 · DOI
- Song H., 2009, The advanced econometrics of tourism demand
- 10.17265/2159-5313/2016.09.003 · DOI · Paper page
- 10.2307/143141 · DOI
- 10.1177/1354816618811558 · DOI
- 10.1016/j.tourman.2014.07.003 · DOI
- 10.1007/978-3-319-51168-9_4 · DOI
- 10.1177/0047287513500391 · DOI
- 10.1371/journal.pone.0154885 · DOI
- 10.1016/j.tmp.2014.01.001 · DOI
- 10.1145/2168752.2168770 · DOI
Cited by
Blanco-Moreno, S., González-Fernández, A. M., & Casaló, L. V. (2026) · Journal of Hospitality and Tourism Technology · doi:10.1108/jhtt-02-2025-0168
Metadata
- Authors
- Gunter, U., & Önder, I.
- Affiliations
- MODUL University Vienna (AT)
- MODUL University Vienna
- ORCID
- Gunter, Ulrich
- Venue
- Journal of Hospitality & Tourism Research
- Publisher
- SAGE Publications
- Published
- 2020-10-13
- Volume
- 45
- Issue
- 2
- Pages
- 373-398
- ISSN
- 0741-5095 (ISSN-L)
- 1096-3480
- 1557-7554
- OpenAlex
- W3093030249
- Cited by
- 38
- References
- 1
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 9 · 2023: 4 · 2022: 8 · 2021: 4 · 2020: 1
- FWCI
- 3.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Hospitality & Tourism Research (publishedVersion)
- Funders
- Hochschuljubiläumsstiftung der Stadt Wien (H-117734/2017)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Business, Management and Accounting › Tourism, Leisure and Hospitality Management › Diverse Aspects of Tourism Research
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Keywords
- geotagged photos, Instagram, tourism demand, tourist arrivals, residents versus visitors, social media engagement, likes and comments, Vienna, urban tourism, location-based data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review