Exploring the potential of Flickr User–Generated Content for Tourism Research: Insights from Portugal

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Users’ country of residence accuracy combining crossing median centre, time zone, and self-reported home location methods_

Country	Accuracy (%)
Portugal	95%
Spain	94%
United Kingdom	85%
United States	91%
France	84%
Italy	83%
Germany	70%
Brazil	96%
Netherlands	62%
Canada	79%

Number of visits by Flickr users’ country of residence (2010–2022)

Nr. Visits	Total	Spain	United Kingdom	France	United States	Italy
1	79%	69%	77%	81%	83%	87%
2	12%	17%	13%	13%	9%	9%
3	4%	6%	3%	2%	4%	3%
4	2%	3%	2%	2%	2%	1%
5	1%	1%	1%	1%	1%	1%

Users’ country of residence using centrographic measures (mean and median centre)

Countries	Total	Median - Accuracy (%)	Media - Accuracy (%)
Portugal	2659	88%	63%
Spain	2001	83%	56%
United Kingdom	1477	65%	32%
Italy	771	64%	29%
France	772	66%	36%
United States	808	53%	62%
Germany	654	56%	21%
Brazil	541	55%	53%
Netherlands	455	48%	24%
Canada	252	45%	58%

Methods used to distinguish visitors and residents through Flickr photos_

Geographic scale	Authors	Study areas	Method
Urban	(De Choudhury et al., 2010)	Barcelona, Spain; Paris, France; London, UK; San Francisco, New York City, USA.	A time span of 21 days between the first and last photo taken and at least two POIs visited in the same city to be identified as a tourist
	(Straumann et al., 2014)	Zurich	A semi-automated methodology to classify the user location attribute of Flickr user profiles was employed (extract the countries of residence)
	(Kádár & Gede, 2013)	Budapest	Used a threshold of 5 days. If this difference is smaller than 5 days, the user can be considered a visitor; otherwise, he/she is a local.
	(Yan et al., 2017)	Central Philippines islands region	Classification of tourist vs. non-tourist based on user profiles.
Regional	(Kádár & Gede, 2022)	Danube Bend	Locals if they have an interval at least 30 days long or have at least 4 intervals visit; space–time patterns, user data and profile analysis
	(Girardin et al., 2007, 2008)	Province of Florence	Visitor if all photos are taken within a period of 30 days, and inhabitant if photos have an interval greater than 30 days.
	(Chen et al., 2019b)	China	Data screening, text data similarity calculation, geographical location clustering, and time series data modelling.
National	(Önder, 2017)	Austria	A time span of 30 days between the first and last photo taken to identify tourists.
	(Stepchenkova & Zhan, 2013)	Peru	Photos with the tags “Peru” and “travel” considered indicators of images taken by travelers rather than residents
Global	(Wood et al., 2013)	836 sites in 31 countries around the world	User’s current location was the origin of each trip and used this information to calculate the proportion of photographers originating from each country.

Flickr users’ activity (2010-2022)

Number Photos per User	total of Users (%)	total of Visitors (%)	total of Locals (%)
1	26	26	20
2	11	12	10
3	7	7	6
4	5	6	4
5	4	4	3
6	3	3	3
7	3	3	2
8	2	2	2
9	2	2	2
10	2	2	2
11	2	2	1
12	1	1	1

Algorithm for Flick users’ classification

User type	Nr.	%	Rules
Local/resident	2659	9	if(location(user) equal Portugal) then type = Local
Visitor	12144	41	if(location(user) not equal Portugal) then type = Visitor OR if(timezone(user) not equal Portugal) then type = Visitor
Unknown	15087	50	-
Total	29890	100	-

Flick users by nationality and Guests in Portugal

Country of residence	Number (#) of Flickr users (2010-22)	Users (2010-22) %	Number (#) of Flickr users (2018-22)	Users (2018-22) %	Guests (2018-2022) %
Spain	2001	21	349	20	10,5
United Kingdom	1477	15	306	18	7,9
France	772	8	161	9	6,9
United States	808	8	167	10	5
Italy	771	8	89	5	3
Germany	654	7	147	9	5,4
Brazil	541	6	70	4	4,8
Netherlands	455	5	103	6	2,6
Canada	252	3	41	2	1,4
Belgium	201	2	32	2	1,4
Switzerland	174	2	36	2	1,3
Russia	125	1	18	1	0,6
Ireland	115	1	21	1	1,8
Sweden	103	1	18	1	0,6
Australia	108	1	19	1	0,5
Austria	93	1	16	1	0,5
Poland	77	1	16	1	1
Norway	74	1	15	1	0,3
Denmark	70	1	9	1	0,5
Finland	73	1	15	1	0,3
China	51	1	9	1	1,1
Argentina	46	0	9	1	0,3