ENTER2018 eTourism Conference presentation

1. ENTER 2018 Research Track Slide Number 1
Destination image through digital photography.
Instagram as a data collector for UGC analysis.
Fabiana Baumann, Maria Sofia Lopes and Paulo Lourenço
(Research Note)
Polytechnic of Leiria ESTM/CiTUR, Portugal
Fundação para a Ciência e Tecnologia (FCT)
fabiana.d.baumann@ipleiria.pt
http://citur.ipleiria.pt

2. ENTER 2018 Research Track Slide Number 2
The photographic practice is an essential element in the
touristic experience.
Destination Management Organizations (DMO) finally
understands that it is no longer the main responsible for the
visual content available about the destination.
What are the tourists sharing about Lisbon on Instagram? Are
the images similar to those shared by the DMO?

3. ENTER 2018 Research Track Slide Number 3
The photographic practice is an essential element in the
touristic experience.
Destination Management Organizations (DMO) finally
understands that it is no longer the main responsible for the
visual content available about the destination.
What are the tourists sharing about Lisbon on Instagram? Are
the images similar to those shared by the DMO?

4. ENTER 2018 Research Track Slide Number 4
The photographic practice is an essential element in the
touristic experience.
Destination Management Organizations (DMO) finally
understands that it is no longer the main responsible for the
visual content available about the destination.
What are the tourists sharing about Lisbon on Instagram? Are
the images similar to those shared by the DMO?

5. ENTER 2018 Research Track Slide Number 5
Aim: to compare DMO official photos with Tourist’s photos
of Lisbon on Instagram by its similarity/dissimilarity among
different attributes.

6. ENTER 2018 Research Track Slide Number 6
Research Questions:
RQ1: Which attributes determine the destination image of
Lisbon according to the visual content produced by the DMO
and UGC (Instagram)?
RQ2: What’s the congruence between the two sets of
photos regarding the frequency of the represented
attributes?

7. ENTER 2018 Research Track Slide Number 7
1st step – create a list of categories, to apply to
both groups of pictures and allow us to segment
them by attributes.
2nd step – Literature Review > who’s comparing
photos of the DMO with the visual content
generated by the user on social media? Who’s
using categories distribution?
Stepchenkova and Zhan (2013) worked with
photos of DMO and Flickr and also used hahstags
to narrow the content as we pretended to do.
Source: Joconde Communications

8. ENTER 2018 Research Track Slide Number 8
January February March April May TOTAL
1.#lisbon #travel 5384 5190 7309 8069 8489 34441
2.#lisboa #vacaciones 83 93 193 341 132 842
3.#lisbona #vacanza 46 41 60 88 50 285
4.#lissabon #urlaub 59 68 154 90 96 467
5.#lissabon #vakantie 11 4 6 20 29 70
TOTAL 5583 5396 7722 8608 8796 36105
Problem of Instagram: for us was that it doesn’t allow a common
user to filter for more than one hashtag and we could also not
define time frame, so we had to create a specific API.
Although we have tried different tests, for different combinations, in
several languages, we ended up using #travel #lisbon, since the
other combinations were not proportionally significant for the
sample.
Source: Own Source
FIG.1 – Hashtags Combinations Test

9. ENTER 2018 Research Track Slide Number 9
STAGE
1
Qualitative content analysis; sample with 300 photos
from June not considered for final sample (Ji, 2011);
free distribution/categories cannot be pre-established
by the evaluators.
STAGE
2
List of categories, based on Stage 1 and the categories’
distribution used by Echtner and Richie (2003) and
Stepchenkova (2013). List of subcategories has
emerged.
List Of Categories | Process

10. ENTER 2018 Research Track Slide Number 10
STAGE
3
STAGE
4
Final list of categories was established serving
both DMO and UGC/Instagram. Each photo
can be evaluated in more than one category or
subcategory, but not in different
subcategories belonging to the same
category.
Test among independent users on a sample of 100
photos. The aim is to see if the grid applies to the study
(Krippendorf, 2004; Neuendorf, 2002). Categories were
adjusted and some subcategories added (Donaire, J.
Camprubí, R. & Galí, N., 2014).
List Of Categories | Process

11. ENTER 2018 Research Track Slide Number 11
PEOPLE
STREET
ARCHITECURE &
BUILDING
LANDSCAPE
GREEN SPACES
TOURISM
FACILITIES
WAY OF LIFE
HOST ONLY | TOURIST ONLY | TOURIST AND
HOST
URBAN | CITY AND RIVER | OTHER
Final List Of Categories

12. ENTER 2018 Research Track Slide Number 12
EVENTS, ART
&
PERFORMING
ARTS
CULTURE,
HERITAGE &
TRADITION
FOOD &
BEVERAGE
SHOPPING &
RETAIL
ATTRACTIONS
EVENTS| FORMAL PERFORMANCE| INFORMAL
PERFORMANCE | STREET ART | OTHER > EVENTS/
NO EVENTS
CRAFTS | ELÉTRICO/TRAM | FADO |NATIONAL
SYMBOLS | HISTORICAL PRODUCTS | OTHER >
NATIONAL SYMBOLS & HISTORICAL PRODUCTS
MONUMENT | PUBLIC SPACE | MUSEUM | SPOTS
Final List Of Categories

13. ENTER 2018 Research Track Slide Number 13
DMO
409 FOTOS
OFFICIAL PHOTOS
Source:www.visitlisboa.com
DMO – Website and Social Media

14. ENTER 2018 Research Track Slide Number 14
UGC
INSTAGRAM
(INSTAGRAM API)
#TRAVEL #LISBON
SAMPLE: 510*
Sample considered p=q=0,5,
N=34.441, E=4,3% and 95%
confidence interval (Z1-α/2).
*A systematic random sampling was used.
UGC/Tourists on Instagram

15. ENTER 2018 Research Track Slide Number 15
Categorias DMO (N=409)
DMO
(%)
Instagram
(N=431)
Instagram (%) Total (N) Total (%) Chi-squarea p value*
People (PPL) 28 6,8% 96 22,3% 124 14,8% 39,967 0
Street (STR) 15 3,7% 43 10,0% 58 6,9% 12,996 0
Architecture & Building
(ARC/BLD)
117 28,6% 179 41,5% 296 35,2% 15,362 0
Landscape (LDS) 75 18,3% 105 24,3% 180 21,4% 4,524 0,033
Green Spaces (GS) 21 5,1% 34 7,9% 55 6,6%
Tourism Facilities (TF) 41 10,0% 81 18,8% 122 14,5% 12,999 0
Way of Life (WOL) 49 12,0% 83 19,3% 132 15,7% 8,391 0,004
Events, Arts & Performing
Arts (EA&PA)
78 19,1% 45 10,4% 123 14,6% 12,505 0
Culture, Heritage &
Tradition (CHT)
85 20,8% 90 20,9% 175 20,8%
Food & Beverage (F&B) 31 7,6% 30 7,0% 61 7,3%
Shopping & Retail (S&R) 11 2,7% 24 5,6% 35 4,2% 4,356 0,037
Attractions (ATT) 147 35,9% 137 31,8% 284 33,8%
adf= 1 in all tests.
*Results significant at 0,05 level are shown.
Source: Own Source
FIG.2 – Categories by frequency: Chi-Square. Congruence of DMO VS UGC

16. ENTER 2018 Research Track Slide Number 16
FIG.3 – Sub-categories: confruence DMO VS UGC
Host Only 19 67,9% 19 19,8% 38 30,6%
Tourist Only 3 10,7% 72 75,0% 75 60,5%
Urban 12 16,0% 24 22,9% 36 20,0%
City & River 29 38,7% 39 37,1% 68 37,8%
Other 34 45,3% 42 40,0% 76 42,2%
Events 74 94,9% 1 2,3% 75 61,5%
No Events 4 5,1% 43 97,7% 47 38,5%
Crafts 15 17,6% 18 20,0% 33 18,9%
Tram 17 20,0% 32 35,6% 49 28,0%
Historical 7 8,2% 14 15,6% 21 12,0%
Fado & 5 5,9% 8 8,9% 13 7,4%
Other 41 48,2% 18 20,0% 59 33,7%
Monument 50 34,0% 66 48,2% 116 40,8%
Public Space 31 21,1% 45 32,8% 76 26,8%
Museum 50 34,0% 7 5,1% 57 20,1%
Spot 12 11,0% 19 13,9% 35 12,3%
Landscape
(LDS)
Subcategorias Total (N) Total (%)
Qui-
quadrado
df (graus
de
liberdade)
0,00285 90
Valor p*
35,583 2 0
5 5,2%
0
28 96
75 105
4478
11 8,9%
137147
101,85 1 0
16,727 4
Attractions
(ATT)
37,176 3
OGD
Frequência
Instagram
(%)
Total OGD
(N)
OGD (%)
Instagram
Frequência
Total
Instagram
(N)
Events,
Arts &
Performing
Arts
(EA&PA)
Culture,
Heritage &
Tradition
(CHT)
People
(PPL) Tourist and
Host
6 21,4%
Categorias

17. ENTER 2018 Research Track Slide Number 17
 We were able to identify main attributes of Lisbon’s destination image
and validate a final list.
 The congruence of both sets of photos was measured by frequency.
There was agreement on the main categories - Architecture & Building,
Attractions, Culture, Heritage & Tradition e Landscape.
 Architecture and Way of Life are shared in a higher percentage by UGC
photos.
 The main difference on subcategories is in Events, Arts & Performing Arts
(EA&PA) – Events/No Events.
Conclusions

18. ENTER 2018 Research Track Slide Number 18
LANDSCAPE
CULTURE, HERITAGE &
TRADITION
ATTRACTIONS
PEOPLE
EVENTS, ART & PERFORMANCE

19. ENTER 2018 Research Track Slide Number 19
Limitations
 Human category coding and analysis > Automated Analysis is crucial for
development.
 Tourist identification (independent user analysis/metadata/profile information) -
considering that the main reason of this study is to compare DMO visual content
with tourists generated content on Instagram, there has to be done a greater
effort on identifying and validating this group within Instagram Users.
 Not all people have Instagram or share photos when they travel. So this is always
a very particular study which has to be completed by other analysis for greater
conclusions.
 A new comparison has to be made using the same platforms for both groups of
photos, larger number of pictures and time frame.
 Instagram API access is very strict. So for us it was very difficult to develop the
work by our own.

20. ENTER 2018 Research Track Slide Number 20
Instagram has more than 800 M users now, being a privileged mobile
App for photo sharing.
It is believed that Instagram is having a great impact on Tourism
Promotion and there’s a great interest of the DMO and tourism
stakeholders in accessing more information regarding the platform.
Visual and sentiment analysis of visitors photos on Instagram might
be very valuable to complete other DMO insights.
Call For Research

21. ENTER 2018 Research Track Slide Number 21
Thank you!

22. ENTER 2018 Research Track Slide Number 22
This work is financed by national funds through the FCT – Fundação para a
Ciência e a Tecnologia, I.P., in the scope of the project
UID/GES/04470/2016.