What Insights Can Google Trends Provide About Tourism in Specific Destinations?

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Dec 4, 2013 (3 years and 8 months ago)

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What Insights Can Google Trends Provide About Tourism in Specific Destinations?


What Insights
Can
Google Trends

Provide About
Tourism in Specific
Destinations?

Summary

These pages
explore the potential of using data on the internet search behaviour of visitors to
inform strategic decision making

relating to
tourism destinations
. In pa
rticular,
they look

at Google
Trends
which includes information back to January 2004
on searches related to tourism from the

World’s most used search engine.

The information here is based on a paper from the Tourism Intelligence Unit (TIU) of the Office fo
r
National Statistics (ONS) which was drafted for the 2nd International Conference on the
Measurement and Economic Analysis of Regional Tourism.
The website for the conference includes
links to the original paper, the related presentation and a series of o
ther papers about improving the
measurement of tourism and its economic impact. The site is at:
http://www.inroutenetwork.org/conference/2011/papers
-
presentations
.

Introdu
ction

Google Trends provides weekly information about internet
searches

with

a
dvanced features

freely

available
at
http://www.google.com/insights/search/
. Users can view and download search volume
patt
erns for one or more
search
term

and this information is also available
broken down
by
the
location of those making the search and by the category

that

the search relates to

(e.g. travel in
general, accommodation or air transport in particular)
.

There is

also
information about
the top and rising searches that include the search term (and
category, if used).

T
he
information from Google Trends is extremely
up to date

as it provides
weekly
figures
for a period

up to and including the current (
even
incomplete
) week
.
The availability of d
ata
from 2004
onwards
allow
s

a time
-
series to be built up relating to particular search terms.

Because of
the
timeliness

of Google Trends
,
there has been a range of studies that examine how
the data

can be used to monitor econ
omic trends

as they happen,
described
as “nowcasting” in
some papers. This

avoid
s

the time lag that is a feature of official statistical releases.

As part of these
pages

we provide

a summary of some of these studies and, in particular, highlight the findin
gs
that
are of particular relevance to

tourism.

Our

main focus is on

the potential for

Google Trends
to

provide
information
about
visitor

behaviour

at
the

local

or destination

level

where it

is not captured by existing surveys
.
We include

an
introduction t
o Google Trends coupled with an explanation of its potential pitfalls and

other caveats.
We then
compare patterns of

search data

from Google Trends with
visitor statistics

from
some of
the
UK surveys
related to tourism
that
do
include local i
nformation, pr
oviding a justification for the
possible utility of

Google Trends for local and destination level tourism
organisations
.

The core of
this work
, therefore,

presents examples of the use of Google Trends
in providing

insight
into the characteristics of poten
tial visitors

to destinations

and of how they search for information
about particular locations.


Section 1:
Key Facts

About Google Trends

In this
and following
section
s
, we explain the
basic features

of Google Trends
.
Before
outlining
some ‘health warnin
gs’ that users should be aware of,
we present some

key fact
s
about Google
trends that are useful for those using the service
:

There are two web addresses for Google Trends

One of these
, “Insights for Search”

requires the setting up of a Google account but
this is free of
charge and provides a full range of features. These include ways of filtering data and the ability to
download
information
. This is the site that we will focus on
here

and it has the following web
address:

http://www.google.com/insights/search/
.


A
more limited
version of
Google Trends is

located

at
:

http://www.google.co.uk/trends
.


The data are measures of

the likelihood of searches

Googl
e

Insights for Search


analyses a portion of Google web searches to compute the number of
searches that have been
carried out
for specific terms, relative to the total number of searches
for
the same term
on Google over time. This analysis indicates the l
ikelihood of a random user
searching for a particular term from a certain location at a certain time.
Google’s

system eliminates
repeated queries from a single user over a short period of time, so that the level of interest isn't
artificially impacted by t
hese
types

of queries.

Google Trends uses relative rather than absolute volumes

The data in

Insights for Search” are displayed on a scale of 0 to 100 after normalisation

(see below)
,

and e
ach point on the graph has been divided by the highest point
. Hence
, if,
when looking at

2009,
a term was most searched for in the first week of June, the annual chart of such searches would
have a peak of 100 in that week and all other weeks would be displayed as proportions of the
volumes of searches for the peak week.

Normalisation
means that
Google has

divided sets of data by a common variable to cancel out the
variable's effect on the data.
This ensures that

the underlying characteristics of the data sets
can

be
compared.
This means that,
for example,
when
looking at

Google Trends data
for

two different
locations
, “interest” (proportion of searches) rather than “volume” is being compared.

Section 2:
Basic
Methods

of Using Google Trends

There are three methods of using Google Trends to make comparisons
and each of thes
e has filters
that improve the analytical capacity of the tool
:

i.

Perhaps most basically, users can focus on one or more search term and filter by type of
search, location of person making the search, date and category.

ii.

Alternatively, it is possible to sele
ct one or more locations

of people making searches
,
compare the interest in a specific search term in these places and filter by type of search,
date and category.

iii.

Finally, users can choose one or more time range (e.g. individual years or months),
compare

the interest in a search term in these periods and filter by type of search,
location and category.

Each

of these methods produces a chart of “interest over time”, details of interest by
area (
country,
sub
-
region or city
) and lists of the top and rising s
earch terms that relate to the selected term. The
time chart compares search terms, locations of those making searches or time periods, depending
on the option selected.

Figures 1
, 2

and 3 in the

pdf
download

are

examples of parts of the outputs from each
of the three
methods.


Figure 1 illustrates the proportion of UK searches for all aspects of travel relating to a
particular
London attraction, Kew
Gardens.

This

has been decreasing slightly
in the years from

2004 to date.
The chart also shows how such int
erest is most prevalent in the spring months.
Google Trends
u
sers are given an option to include forecast
s which are shown in
F
igure 1. These

are only based
upon
the trends shown rather than any other
, external,

considerations.

Figure 2 shows an analysis o
f the search term “Cardiff” from January 2007 onwards, restricted to
the travel category and to searches from three European countries. The chart illustrates that,
generally, interest has been proportionally greater in the Netherlands than in France and Ge
rmany.
The most notable of the exceptions to this pattern was in the autumn of 2007 when Cardiff hosted a
Rugby World Cup quar
ter final that included France.

Other spikes in interest
in France
also coincide with international rugby matches in Cardiff. The
spikes in interest from the Netherlands are not as easily explained by sporting events but a lot of
fluctuations in such data are likely to be due to the fact that the information is based on relatively
small volumes of searches.

Despite these fluctuations
, this particular analysis from Google Trends
has clear potential for
targeting

and monitoring the effects of overseas marketing activity.

Figure 3 again focuses on Kew Gardens, comparing each of the last four full years of the data in
F
igure 1. This repli
cates the seasonality in the proportion of searches that was shown in the
previous chart but includes more detail. Patterns relating to public holidays and the periods outside
of school terms are noticeable but other peaks may relate to special events, mar
keting campaigns
or news stories and this type of chart is a useful measure of the impact of such publicity.

The travel category used in these and other analyses in this
pages

includes
sub
-
categories relating
to hotels and accommodation,
attractions and ac
tivities,
bus and rail travel, air travel, car rental and
taxis, cruises and charters, adventure travel and vacation destinations.

Google Trends allows users
to select these sub
-
categories
and use them in the same way as categories. Figure
s

2
0

and 21, for
example, chart

the hotel and accommodation sub
-
category.

Section 3:
Caveats

to be Considered When Using Google Trends

This
section
highlight
s

issues that need to be considered when using Google Trends data. Some of
these
are caveats that
occur because
of
the way that
the data are obtained and presented.
One

example

of this is

the use of
relative rather than absolute volumes of searches
. We

have highlighted
this
in
the
previous
section
but i
t is worth restating
and
emphasising that Google Trends
normalises
data when comparisons are made.


Normalisation

means that
, for example,
while
“Insights for Search”
suggests

that
interest in travel to
France is very similar in Wales and England
, as highlighted in Figure 4 in the pdf

download
.
However,

the
large
differen
ces in the relative populations
lead to

volumes of Google searches
for
this topic in England
being

larger.

Another caveat is that
Google uses IP address information to “make an educated guess” about
where queries originated
. I
n the UK this results in infor
mation based on
som
e rather unexpected
conurbations

and, c
oupled with normalisation, this methodology means that conclusions
about
interest within sub
-
regions
based on the data have to be explained carefully.

Other considerations to be aware of when using
this tool in a tourism context include the fact that
users of Google may not be representative of all visitors to a destination
as, for example, people
from some demographic groups may be more likely to use a travel agent or
may not have internet
access. I
n addition,

specific
types of touris
ts may use search tools

to a lesser extent
, for example,
regular visitors to a destination or
those visiting friends and families.

Section 4:
Potential
Pitfalls

to Avoid When Using Google Trends

An example of the care t
hat is needed when choosing destination
-
based search terms
relates to an
investigation of the

r
elative interest
to Google users worldwide
of the search terms “York” and
“Edinburgh”
. Interest

appear
s
, initially,

to
be much

greater for
the

English historic c
ity than its
Scottish counterpart
, as illustrated in Figure 5 of the pdf

download
. However,
analysis of the most
used search terms for “York”
(Figure 6)
indicates

that a large proportion of the interest in it actually
relates to New York and versions of it
s name in other languages.


Google Trends allow users to compare search terms with related content excluded by using a minus
sign. By using this method to omi
t “new”, “nueva” and “nova” from searches for York, a more
realistic comparison of interest in Edi
nburgh and York is possible, with the former receiving a
greater proportion of searches
, as shown in Figure 7
.
It is

possible that data still include searches
for places named or including York (and Edinburgh) other than those in the UK but it is unlikely
that
any of these have as misleading an effect as New York
.

Where destinations are among those with low search volumes, Google Trends data cannot be
broken down by category. In these cases it is important to consider whether searches for a
particular
desti
nation
may be for other purposes than tourism.
For example,
interest
in Tintern
Abbey, the

historic visitor attraction

in Eastern Wales
,

includes searches relating

to

a poem by
William Wordsworth
with the Abbey’s name in its title. A
djustments to the searc
h term
have to be

made to exclude elements relating to this poem
if tourism interest only is being assessed.

A further consideration when using Google Trends is to take account of whether a specific location
has the same name in other languages and to incl
ude these in search terms.
F
or example,
the
majority of
Google users in France
undertaking searches

for travel relating to London
use its French
name “Londres”
.

However, a significant minority (including English speaking ex
-
patriots) use
“London”
.

Sectio
n 5:
Comparison
of Google Trends
with
Official

Data

One of the potentially useful facets of Google Trends is that it provides some information about
demand or interest for destinations that are
too small to be

covered by national surveys.
The
pdf

download
,

however, includes
charts about

two sets of
currently available official data

and compares
them with
related
information from Google Insights for Search.

F
igure
8

in the pdf
shows

annual
admissions data
from Visit England

(the national tourism
organisation

for England)

for
two of the UK's leading attractions
;

the Eden Project in Cornwall and
Kew Gardens in west London
.

The figure

indicate
s

that
the number of
a
dmissions for the former ha
s

fallen in each year from 2004 to 2010

while the pattern for the latter

has been
less pronounced
with
fluctuations and a decrease

in
the most
recent year
.

Th
e related chart in F
igure
10

in the pdf
shows a similar pattern

in the Google chart
:
a more
noticeable
reduction

in interest in the Eden Project
(
as measured by travel r
elated searches in the
UK
)

than in
interest in
Kew Gardens
. However,
by this measure, interest in the latter
has also
fallen
slightly in recent years. The Google chart does include 2011 data which appear to show similar
levels to that in 2010 for both attr
actions and it will be
instructive

to compare this with
the
2011
annual
admissions data
set

when it is available
.

The
chart
in

F
igure
9

looks at the quarterly estimate of the number of visits of US residents to
London from the
UK’s
International Passenger S
urvey

(IPS
-

carried out by the Office for National
Statistics)
. This can be
compare
d

with trends in
American
searches
that include

the term “London”
and

relate to hotels and accommodation

(see
F
igure
11
)
.
The published data

highlight the
seasonality of vi
sits from the USA and indicate that the total number of visits in 2010 was less than
in
the previous year.
The Google Trends output

shows a similar pattern of interest by quarter and
also a falling of interest year on year which has continued into the seco
nd and third quarters of 2011,
periods for which

IPS data
were not

available

when the c
h
a
rt was produced
.

Section 6: Examples of
Seasonality

in Google Trends

This
section
and
those following

include a variety of presentations of Google Trends data to
high
light how the tool can assist with analysis of aspects of tourism to a number of different types of
destination.

The
pdf
download

includes

three
charts
from Google Trends
of interest over time
for each year from
2007 to 2010. These reflect UK
-
based
travel
-
related
searches

for
a coastal resort (Skegness), an
inland holiday destination (the Lake District) and a major city (London).

The fourth chart
in
the pdf

investigates the seasonality of
travel
-
related
interest in
Glasgow and Edinburgh,
the two largest
Sco
ttish cities
.

Figure 12 in the pdf

indicates that
the
period where there is the greatest
travel
-
related
interest in
Skegness coincides with July and August, the months that include school holidays. There are also
spikes of interest in April, which usually
includes an Easter
school
break. The chart for the Lake
District
(Figure 13)
has
peaks that are
less pronounced, although the periods of greatest interest
are
the same as for Skegness. It also highlights that interest in travel to the area has fallen durin
g the
past four years, in contrast to the trend for the coastal resort. The equivalent chart for London
in
Figure 14
shows comparatively little seasonal variation in Google searches and, again,
seemingly
falling interest from 2007 to 2010.

The patterns ove
r time for travel
-
related interest in Edinburgh and Glasgow in the
Figure 15

are very
similar. The former is the subject of a higher proportion of searches from UK
-
based Google users
but in both cases there are peaks of interest each year at the start of t
he third quarter

and another
upturn in interest at the turn of the year
.
Possible explanations for the patterns in the chart would
include the internationally renowned Edinburgh Festival each summer and the New Year’s Eve
celebrations. The resemblance betw
een the trends for each city may suggest that the festival
prompts searches
relating

to Glasgow as well as
to
Edinburgh.

Section 7: Examples of the I
mpact of Major Events

in Google Trends

A major event

that has an effect on levels of travel
-
related intere
st for a specific location is the
Glastonbury
M
usic Festival which takes place in June of most years.
Figure 16

in the pdf
download
illustrates that searches relating to all aspects of travel and “Glastonbury” peak dramatically at
around the time of the fe
stival. Smaller peaks are likely to relate to announcements of when tickets
go on sale

or of announcements of which acts are performing. The absence of a festival in 2006
also gives an indication of the levels of
travel
-
related internet searches

relating t
o
the Somerset
town for purposes

other than the festival
.

Figure 17

focuses on the small town of Castle Cary which is the location of the nearest railway
station to the festival, about eight miles away. Interest relating to travel to this town peak
s

in Ju
ne of
each year that the festival takes place but the relative high levels of searches in 2006 and other
parts of other years suggest that Google users are also interested in non
-
festival related aspects of
the town.

The Google Trends tool also allows anal
ysis of the amount of interest
that
smaller, non
-
music based
events prompt for specific areas. The example in
F
igure
1
8

is the Welsh town of Abergavenny which
hosts a food festival in the third weekend of September each year.

Because of the comparatively
s
mall number of searches relating to this location the analysis is not limited to a specific category. In
each of the three years covered by the chart, interest in “Abergavenny” peaks in September and the
degree of interest in the town

at the time of the fe
stival
, as measured in this way, is similar.

Section 8: Examples of the Impact of
News Stories

in Google Trends

Positive News Stories

In the summer of 2010, Rhossili Bay on the western tip of the Gower Peninsula in south Wales was
voted best British beac
h. Figure
1
9

in the
pdf
download

highlights how this award prompted an
increase in interest in the beach at about the time the news story was released. However, the chart
also indicates a higher level of interest in the summer of 2011 than in 2008 or 2009,

perhaps
suggesting that the award has increased the profile of the beach. The comparator in the chart is a
neighbouring beach, Port Eynon, which received the same honour in the summer of 2011.

Major

News Stories

Riots in London and other English cities to
ok place in early August
2011
and there was widespread
international coverage of these. The
re was a lag until the

data that show any effects on the number
of tourism visits
were due to be

released

but Google Trends
was

a useful up to date indication of
any

impact
on the
level of
interest in
searching for
travel

information for

London.

Figure
s

20

and 21
in the
pdf

feature

two charts of accommodation
-
related searches that include the
term “London”. These relate to US and UK residents, respectively. In both c
ases there is a distinct
fall in the level of interest in

the second half of August 2011. As the tables include more than one
year’s data, we can establish that the recent drop in interest does not mirror what happened in 2009
and 2010 and could therefore
be a reaction to the riots.

Another major news story in London during 2011 was the royal wedding at the end of April. Interest
in London accommodation among Americans has a particularly noticeable spike for the week
containing the wedding day, the only wee
k where such interest was higher in 2011 than in the
previous two years. The peak was
not particularly striking in the chart
for UK residents but
a
noticeable trend over 2011 is that interest among UK residents is generally higher than in 2009 and
2010.

Th
is could be a reflection of a greater likelihood to investigate domestic holiday options rather
than overseas equivalents for economic reasons.

Section 9:
Implications of
Previous
Research

The availability of indices analysing the queries made using the w
orld’s most popular search engine
has prompted
many
researchers
and analysts
to examine the extent to which Google Trends can be
used as an indicator of economic activity
. This section introduces and summarises recent reports
that
focus on

the tool

and hig
hlights the research within these that has implications for tourism
.
Full
references of these and other papers
are in a later section
.

One of the
key introductory

papers on these topics was published by two economists affiliated to
Google.
Predicting the P
resent with Google Trends

(
Choi and Varian
, 2009) investigated whether
search queries
could
help "predict" current economic activity,
in other words,
rep
ort it without a lag.
The report aimed

to familiarize readers with Google Trends

and to

illustrate some simple forecasting
methods that use
its

data
. The paper included analysis of Hong Kong visitor data and found that
Google searches on `Hong Kong' were p
ositively related to arrivals. In addition, the paper concluded
that arrivals in
the destination

in the previous month were positively related to arrivals in the current
month, as were arrivals 12 months
earlier
. The authors also
explained that during the
Beijing
Olympics travel to Hong Kong decreased.

A second Google paper,
On the Predictability of Search Trends

(
Shimshoni, Efron and Matias
,
2009), provided
the analysis that resulted in a basic forecasting capability being introduced into
Google Insights for Search (as shown in
F
igure 1). The paper found that over half of the most
popular Google search queries
were predictable using the method that the author
s selected. The
analysis

included an assessment of the predictability of 10 broad search categories and concluded
that, of these, only health was more predictable than the two tourism
-
related categories of food
(
and
drink
)

and travel.

T
he ONS Economic and
Labour Market Review (ELMR)
in the UK
followed up the above analys
e
s
with a detailed investigation of the use of Google Trends data for various search categories
:
Googling the Present

(
Chamberlin
, 2010). The article looked
at

the
correlation with official data
of

over 30 categories of retail sales, property transactions, car registrations and foreign trips. In terms
of tourism, the
research

found

that

none of the numerous

relevant Google Trends categories were
significant in a regression with
the
numbers of foreign trips
. However, the article did conclude that
the 'Travel' category
showed

similar seasonal movements

to this statistic.

Two
Ruh
r Economic Papers from Germany introduced private consumption indicators based on
search query time series provided by Google Trends.
Forecasting Private Consumption:
Survey
-
based Indicators vs. Google Trends

(
Schmidt and Vosen
, 2009)

used information from
US
-
based searches and compared this to the two most common American survey
-
based indicators.
A second paper,
A Monthly Consumption Indicator for Germany Based on Internet Search Query
Data

(
Schmidt and Vosen
, 2010), makes similar comparisons between German search data and
European Commission confidence indicators. In both cases the new
search data
indicator
outperformed the survey
-
b
ased indicators.

Within the UK, the Bank of England raised the
profile

of
the discussion of the

possible uses of

Google Trends by highlighting
the topic

in its Quarterly Bulletin. The article,
Using Internet Search
Data as Economic Indicators

(
McLaren
, 2011)
focussed on the UK labour and housing markets and
includes a useful summary of the potential benefits and problems of internet search data. It also
referred to a
nalyses elsewhere, including the Bank of Israel paper
Query Indices and a 2008
Downturn

(
Suhoy
, 2009)
. This tested the hypothesis that Google query indices may be helpful in
d
rawing inferences about the state of current economic growth and confirmed that Israeli data
supported the hypothesis.

As well as focussing on the whole economy, there have also been papers that concentrate on
how
Google Trends data can be used within
a sp
ecific
part

of the economy,
including

tourism or tourism
industries.
Forecasting Tourism in Dubai

(
Saidi, Scacciavillani and Ali
, 2010)
, for example,

uses
traditional empirical methods as well
as analysis of Google Trends. It concludes that the latter is
helpful for improving the long
-
term forecast for guest nights but has less short
-
term usefulness. It
also
highlights the effectiveness of air travel searches in forecasting arrivals at Dubai air
port.

An earlier tourism
-
related paper examined
the behaviour of
users of the Dogpile.com search engine
rather than
of
Google. This paper,
An Analysis of Travel Information Searching on the Web

(
Jansen,
Ciamacca and Spink
, 2008), conclude
d

that, in 2005 at least, about 6.5% of Web queries were for
travel searching

and that

geographical
information accounted for nearly 50% of
this.


Other recent research includes a UK study (
Judge

and

Hand
, 2010) that found clear evidence that
Google Trends data on searches relevant to cinema visits could increase the accuracy of cinema
admissions for
ecasting models.
Another paper for the Journal of Travel Research (
Fesenmaier and
others
, 2011) proposed and evaluated a three stage model to examine ho
w online travellers use
search engines and how aspects of the travel planning process shape this use.

Finally, Google Trends is not the only aspect of internet usage that has
prompted research. In terms
of tourism, a good example of further analysis is
a
paper

(
Milano and others
, 2011)

that
measures

the impact that
the social media sites
Facebook and Twitter have on the

popularity of tourism
websites.

More generally, a paper from the University of Illinois (
Xin Jin and others
, 2010) discussed
the potential of using the
photo
-
sharing site

Flickr
f
or
forecasting.

Conclusion

& References

Google Trends provides
an effective

measure of levels of inte
rest in
one or more

topic
s

and of
changes over time in this interest.
These pages

highlight how
useful this can be for those involved in
analysing
tourism, particularly at a local or destination level.
They include

examples
that match
patterns in internet
search terms with aspects of tourism. Finally,
we have

also summarise
d

research papers that discuss how Google Trends data can act as a proxy for economic measures
and, in particular, provide more up to date information than
even the most
current data sour
ces can
supply.

References

A Monthly Consumption Indicator for Germany Based on Internet Search Query Data

Torsten Schmidt, Simeon Vosen (2010)

Rh
einisch
-
Westfälisches Institut

http://ideas.re
pec.org/p/rwi/repape/0208.html


An Analysis of Search Engine Use for Travel Planning


Daniel R. Fesenmaier, Zheng Xiang, Bing Pan, Rob Law (2011)

http:
//sb.cofc.edu/academicdepartments/hospitalitytourism/facultyandstaff/pan
-
bing.php


An Analysis of Travel Information Searching On The Web

Bernard J. Jansen, Christopher C. Ciamacca, Amanda Spink (2008)

http://academic.research.microsoft.com/Publication/6113425/an
-
analysis
-
of
-
travel
-
information
-
searching
-
on
-
the
-
web


Do Google Searches Help in Nowcasting Private Consumption?

Ko
nstantin A. Kholodilin, M
.

Podstawski, B
.

Siliverstovs (2010)

Deutsches Institut für
Wirtschaftsforschung

http://ideas.repec.org/p/diw/diwwpp/dp997.html


Forecasting Private Consumption
-

Surv
ey
-
based Indicators vs. Google Trends

Torsten Schmidt, Simeon Vosen (2009)

Rheinisch
-
Westfälisches Institut

http://ideas.repec.org/p/rwi/repape/0155.html


Forecasting Tourism in Dubai


Dr. Nas
ser Saidi, Dr. Fabio Scacciavillani, Fahad Ali (2010)

Dubai International Finance Centre

http://www.difc.ae/publications


Google Econometrics and Unemployment Forecasting

Nikos Askitas, Klaus F. Zimmermann
(2009)

Deutsches Institut für Wirtschaftsforschung

http://ideas.repec.org/p/diw/diwwpp/dp899.html


Googling the present


Graeme Chamberlin (2010)

ONS Economic & Labour Market Review

http://www.ons.gov.uk/ons/rel/elmr/economic
-
and
-
labour
-
market
-
review/no
--
12
--
december
-
2010/index.html


On the Predictability of Search Trends

Yoss
i Matias, Niv Efron, Yair Shimshoni (2009)

Google Labs, Israel

http://googleresearch.blogspot.com/2009/08/on
-
predictability
-
of
-
search
-
trends.html


Predictin
g the Present with Google Trends

Hal Varian, Hyunyoung Choi (2009)

Google Research Blog

http://googleresearch.blogspot.com/2009/04/predicting
-
present
-
with
-
google
-
trends.html


Query Indices and a 2008 Downturn
-

Israeli Data
.

Tanya Suhoy (2009)

Bank of Israel

http://www.bankisrael.gov.il/deptdata/mehkar/papers/dp0906e.htm


Sea
rching for the picture
-

forecasting UK cinema admissions making use of Google Trends data

Guy Judge, Chris Hand (2010)

University of Portsmouth Dept of Economics

http://eprints.port.ac.uk/4820/



The Effect
s of online social media on tourism websites

Roberta Milano, Rodolfo Baggio, Robert Piattelli (2011)
International Conference on IT and Travel &
Tourism

http://www.iby.it/turismo/


The Wisdom of Social Multimedia

-

Using Flickr for prediction & forecast

Xin Jin, Andrew Gallagher, Liangliang Cao, Jiebo Luo, Jiawei Han (2010) ACM Conf. on Multimedia

http://www.ifp.illinois.edu/~cao4/publications.htm
l


Using internet search data as economic indicators

Nick McLaren, Rachana Shanbhogue (2011)

Bank of England

http://www.bankofengland.co.uk/publications/quarterlybu
lletin/m11qbcon.htm