locating central travelers’ groups in travel blogs’ social networks (accepted journal of...

8/9/2019 Locating Central Travelers Groups in Travel Blogs Social Networks (accepted Journal of Enterprise Information Man

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Locating Central Travelers Groups in Travel Blogs Social

Networks

Vasiliki Vrana,

Assistant Professor, Department of Business Administration

Technological Institute of Serres, [email protected], Terma

Magnisias, 62124 Serres, Greece

Kostas Zafiropoulos,

Assistant Professor, Department of International and European

Studies, University of Macedonia, [email protected], Egnatia 156, 54006

Thessaloniki, Greece


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Locating Central Travelers Groups in Travel Blogs Social

Networks

Abstract

Purpose The paper, using Travelpod.com, aims to provide a

methodology to locate central groups of travelers and to describe

pattern characteristics of central travelers.

Design/methodology/approach The paper uses snowball sampling

to locate travelers and analyze their hyperlinks interconnections to

identify central travelers groups. Analysis of the adjacency matrix of

the social network of travelers using Multidimensional Scaling and

Hierarchical Cluster Analysis to identify core travelers groups

follows.

Findings Seven % of travelers are considered as central travelers.

They form core groups containing the most active and information

providing travelers. Group membership is correlated with common

travelers characteristics.

Research limitations/implications Research is limited to a

specific network of travelers, to a specific time interval, and to a

specific sampling method. Repetition of the study in other travelers

networks in several time instances using a full list of member

travelers would help to generalize findings. Also other graph

theoretic approaches, besides the statistical analysis used, could

reveal more properties.


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reviews and recommendations are becoming the new digital form of

word of mouth (Litvin et al., 2008; Schmallegger and Carson, 2008).

Sigala (2009, p.221) mentioned as information is the lifeblood of

tourism, the use and diffusion of Web 2.0 technologies have a

substantial impact of both tourism demand and supply. Moreover,

blogs are becoming a very important information source for interna-

tional travelers for getting travel advice and suggestions. Blogs can

impact on travelers evaluation of tourism products and decision

making process. when reading others travel experiences

through weblogs, this also creates to the reader the willingness to

travel and visit the same destination claimed Sigala (2009, p.225).

Xiang and Gretzel (2010) also highlighted the functions of blogs in

creating and sharing new experiences, trustworthiness to online

travelers and the use of blogs as marketing intelligence.

Travel and tourism blogs take several different forms, but the

majority of travel blogs are diaries of individuals about their trips

(Lew, 2007). Many public travel blog sites, like travelblog.org,

travelpod.com, blog.realtravel.com, yourtraveljournal.com,

worldnomads.com and travelpost.com, have specialized in hosting

these individual travel blogs (Pan et al., 2007; Schmallegger and

Carson, 2008). Travel blogs can include comments, suggestions,

advice, directions, maps, photos and videos, links to related websites

hyperlinks, to external information, links to other travelers, RSS,

trackbacks, comments, taglines, archives, permanent links and

blogrolls (Blood, 2002; Sigala 2009). Travelers use blogs as a new

way of sharing tour experiences with family and friends and with an

international audience. Knowledge seeker tourists can get from blogs

virtual experiences of trips, trend information for their destinations


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(Schmallegger and Carson , 2008; Sigala, 2007) and prepare their

trips with confidence using subjective data unfiltered and free of

marketing bias (Hepburn, 2007). Due to the unbiased information

shared in blogs based on first-hand authentic travel experiences,

many travelers tend to use and trust blogs information for searching

for travel information, tips and selecting travel suppliers and

destinations (Sigala 2009, p. 224).

Blogging tools enable between-blog interactivity (Du and

Wagner, 2006) thus social networks are established and relationships

in the world of blogs are built (Efimova and de Moor, 2005; Sigala,

2009). Finding blogs communication patterns is important since The

discovery of information networks among websites or among site

producers through the analysis of link counts and patterns, and

exploration into motivations or contexts for linking, has been a key

issue in this social science literature (Park and Jankofski 2008, p.

62). Further, in the study field of blogging (especially in political

blogging), it has been proved that blogs are organized around central

focal point blogs, where most of the informative conversation is

taking place (Drezner and Farrell, 2004). In this sense, travelers

blogs may also be organized around central blogs. The latest may

serve as the most influential travelers blogs where the most read

posts appear. Thus, it is interesting to provide the tools and

methodology to locate such central blogs groups. The paper aims at

providing a methodology to locate central groups of travelers and to

locate travelers that link to them. Finding central travelers is

interesting and useful since one can locate them and study the

information these influential travelers provide. The paper Favorite

travelers lists and finds connectivity patterns between these groups


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of travelers: patterns of central travelers and patterns of travelers

linking to them. The paper studies travelers interconnections through

hyperlinks by using as a case study Travelpod.com.

2. Conversation in the blogosphereBy definition, blogs link to other sources of information usually to

other blogs. Barger (1997) who first used the term weblog, defined

blog as a web page where a blogger logs all the other web pages

he finds interesting. Drezner and Farrell (2004, p. 5) defined

weblogs as A web page with minimal to no external editing,

providing on-line commentary, periodically updated and presented in

reverse chronological order, with hyperlinks to other online sources.

Blogging tools provide the appropriate enhanced features for

managing blog interactivity and promoting the creation of social

networks among bloggers (Du and Wagner, 2006). Sigala (2008)

based on Ying and Davis (2007) and Lento et al. (2006) studies

mentioned blogs create and maintain strong online communities

through their social ties tools such as blogrolls, permalinks,

comments and trackbacks and Williams and Jacobs (2004)

highlighted that interactivity is the key to the success of blogs and the

other social network systems . Interactivity between blogs can be

implemented with blogrolls, permalinks and comments and

trackbacks. A blogroll is a list of blogs that many bloggers

maintain and occupies a permanent position on the blogs home page.

The list consists of the blogs that the blogger frequently reads or

especially admires and offers links to these blogs. Blogrolls

provide an excellent means of situating a bloggers interests and


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preferences within the blogosphere. Bloggers are likely to use their

blogrolls to link other blogs that have shared interests mentioned

Drezner and Farell (2004, p.7) and Marlow (2004, p.3) wrote This

form evolved early in the development of the medium both as a type

of social acknowledgement and as a navigational tool for readers to

find other authors with similar interests. Albrecht et al. (2007, p.

506) referred to this form as connectedness of weblogs.

Interactivity can be also achieved through permalinks and

comments. Comment tools are allowing readers to express their

opinions on a weblog entry and permalinks are allowing weblogs link

posts with one another. In that way, bloggers and readers can express

thoughts and discuss their opinions through an iterative cycle of

postings and comments (Drezner and Farell, 2004 ; Guo et al., 2008;

Mishne and Glance, 2006). Such posts themselves link directly to a

specific post on the other blog, and are a key form of information

exchange in the blogosphere. Drezner and Farrell (2004) highlighted

the fact that links and page views are the currency of the

blogosphere.

Last, are trackbacks and pingbacks. Trackback is a citation

notification system (Brady, 2005) which informs bloggers of traffic

to their original entries. It enables bloggers to determine when other

bloggers have written another entry of their own that references their

original post (Waggener Edstrom Worldwide, 2006). If both

weblogs are enabled with trackback functionality, a reference from a

post on weblog A to another post on weblog B will update the post

on B to contain a back-reference to the post on A (Marlow, 2004)

creating in that way a symmetrical link the original post (Efimova

and de Moor, 2005). A pingback is an automated trackback.


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Pingbacks support auto-discovery where the software automatically

finds out the links in a post, and automatically tries to pingback those

URLs, while trackbacks must be done manually by entering the

trackback URL that the trackback should be sent to

(http://codex.wordpress.org /Introduction_to_Blogging#Pingbacks)

Conversation in the blogosphere is organized around some

central focal point blogs, an elite minority of blogs that is known as

A-list blogs (Herring et al., 2004; Herring et al., 2005). A-list

blogs were first recorded in the political blogosphere (Drezner and

Farell, 2004) however nowadays some blog authors manage to make

themselves a celebrity among the community of bloggers whatever

the type, purpose, or content of a blog is (Trammell and

Keshelashvili, 2005). A-list blogs receive the most inbound links

from other blogs and are the most widely read and cited. Waggener

Edstrom Worldwide (2006) commenting on A-list blogs wrote

almost every category has a handful of bloggers who are perceived

to carry more weight in terms of influence than the others.

3. The case study and MethodologyTravelPod was founded in 1997 as the world's original travel blog

and it has been identified as one the most popular travel blog site by

many researchers (Carson, 2007; Pan et al.,2007; Schmallegger and

Carson, 2008; Wenger; 2007). On 18th November 2008, TravelPod

was identified through Technorati as the 11th between the 100 top

blogs having an Authority: 9299 and Rank: 9, and is the 1st travel

blog in the list.


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TravelPod introduces itself as: TravelPod's free travel blog

lets you chart your trips on a map, share unlimited photos and videos,

and stay in touch while you travel. Thought TravelPod travelers can

1.preserve travel memories by uploading photos and videos, chart

trips with travel maps and weave photos directly into stories 2. Get

inspired for next trip by meeting other travelers and participating in

travel forums 3. Share experiences with family and friends, by setting

up email import tools, send email updates and RSS feeds and email

notifications for new entries 4. Use advanced features as update

travel blogs from mobile phone, track visitors of blogs, show travel

blogs on MySpace, Facebook and other sites send update

notifications to Facebook friends and others. In TravelPod each

traveler can maintain many blogs presented at travelers TravelPod

page. At this page, Recent Entries, Recent Comments, Recent

Forum Posts, Favorite travelers and Others Similar Travelers

are also presented. Favorite travelers is a special form of a blogroll

and is a list of travelers that travelers frequently read or especially

admire.

Travelpod.com provides a Top 100 travelers list, according

to number of visits to their website. This paper considers the Top 100

travelers list and records links from travelers to other travelers within

TravelPod. The functionality of Travelpod.com regarding Favorite

travelers lists serving as blogrolls, allows to record and study the

incoming links between travelers.

Next, by using snowball sampling, links from these travelers

to new travelers within TravelPod are recorded. Snowball sampling

has also been used in collecting data in political blogosphere and is

one of the preferred methods to sample in social networks studies. By


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considering the Top 100 travelers as a starting point the aim is to

maximize the probability of finding most popular and therefore

perhaps most linked travelers. Finally, a set of 563 travelers and their

incoming links is formed. The recording of travelers and their

hyperlinks was done during January 2009. Regarding the recording

of incoming links between travelers, an automated routine was used.

So an effort was made to keep human interaction during recording to

a minimum level.

The adjacency matrix of the social network of travelers was

used. In this matrix if two travelers I and J have a relation then 1 is

placed at the cell (i,j), otherwise 0 is placed in this cell. The paper

adopts a statistical approach for studying networks, although other

graph theoretic approaches also exist such as finding components or

cliques. These graph theoretic notions are used in the study of social

networks to locate actors who interact with each other. Our statistical

approach on the other hand, in particular scaling and clustering

analysis, is the preferred method because the interest is to find groups

of travelers which have these properties: 1) within these groups the

travelers need not be interconnected but 2) rather, they need to be

linked by nearly the same set of travelers. In this sense, clustering

algorithms applied on the original adjacency matrix are considered

suitable for locating these groups.

The paper studies incoming links between travelers, using

the Favourite travelers list which is equivalent to blogroll. In order

to construct a network, a 563 by 563 non-symmetric binary data

matrix is used where unity is placed in cell ij if traveler i links to

traveler j through the favourite travelers list, else zero is placed in the

cell. The next step involves the construction of a travelers


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interconnection network. It is a directed graph where travelers are

noted as nodes and incoming links as directed arrows. The paper

adopts a method introduced by Zafiropoulos and Vrana (2010) for

locating core blog groups in political blogging. The original idea

(Drezner and Farrell, 2004) is that political blogs are organized

around central focal point blogs, where most of the informative

conversation is taking place. Zafiropoulos and Vrana (2010)

introduced a combination of social networking theory,

multidimensional scaling and hierarchical cluster analysis to locate

such groups by studying incoming links through blogrolls. By

finding such groups, one can explore how bloggers are organized and

easily follow how conversation proceeds. For travel blogs, the idea

may take a different form. Travelers or travel blogs are

interconnected through blogrolls or favourite travelers lists, and in

this way they may form groups of blogs or travelers, which are

considered familiar or most important (while the rest of the blogs or

travelers are more isolated). It is important to locate such groups in

order to see how travelers or travelers blog are organized, which

blogs are considered familiar and which are their characteristics that

distinguish them from the rest of the travelers.

Multidimensional Scaling (MDS) is used in the analysis as a

data reduction technique and also to quantify the original binary data.

The method reproduces the original data and map them on a fewer

dimensions space (namely two in this analysis) while the effort is to

keep intact the distances among the original data on the new

reproduced data. Stress is a measure of goodness of fit between

distances of original data and distances of the reproduced data. Better

fit is assumed when stress is close to zero.


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Hierarchical Cluster Analysis (HCA) uses the quantified data

from MDS to produce clusters of travelers with similar properties.

Since the input is the incoming links, travelers in the same cluster are

linked by nearly the same set of travelers. So in this way the travelers

in a formed cluster are regarded to be of the same family have

common characteristics - by travelers who link them. Some of the

clusters that are produced by HCA, gather the largest number of

incoming links. If this happens then they may serve as conversational

focal points.

4. FindingsMultidimensional Scaling (MS presents very good fit with

Stress=0.0493), followed by Hierarchical Cluster Analysis (HCA)

result to the formation of seven clusters of travelers regarding

incoming links. These clusters of travelers are described in Table 1.

They are called Clusters hereafter. To decide about the suitable

number of out-clusters, the study uses a scree plot of the number of

clusters against Wilks Lambdas.

Only 41 out of 563 travelers, i.e. 7.3% of the 563 travelers

are organized in seven central clusters. Cluster 1 consists of 14

travelers who have an average number of incoming links 5.71.

Clearly, it is the least referred cluster out of the seven clusters since

the travelers within the cluster are referred nearly by six other

travelers. Cluster 2 consists of ten travelers and has an average

number of incoming links of 6.6, which slightly larger than the

relative number of incoming links of Cluster 1. Cluster 3 consists of

seven travelers and has an average number of incoming links 6.86.


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Cluster 4 consists of five travelers with an average number of

incoming links 11.83. Cluster 5 has two travelers luchy and

wakingdream and an average number of incoming links 14. Cluster

6 and 7 both consist of one traveler each, themeoff in cluster 6 with

number of incoming links 16 and whereshegoes in Cluster 7 with

number of incoming links 30, which is the largest of all (Table 1).


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Cluster number and

travelers name in

TravelPod

Number

of

incoming

links

Top100

rating

# visitors

6/1/2009

Travelers country of

origin

# of countries

visited

Posted photos

5/12009

Posted

entries

5/1/2009

In travel

blogs

5/1/2009

1. aaronvdb 6 Australia 10 39 107 5

1. alinmattrtw2005 4 27 129,625 United Kingdom 44 1797 315 1

1. barrettbingley 5 Canada 20 536 77 1

1. bjw1981 4 Australia 13 92 50 2

1. clintonb 5 57 84,401 Australia 33 1084 181 3

1. kevandsian 6 9 204,195 United Kingdom 51 778 201 5

1. marktjhung 9 25 3945 111 4

1. matty 6 Australia 24 125 90 2

1. mills-mcshea 7 United Kingdom 13 950 95 2

1. moses 4 Australia 19 27 64 2

1. racheljohn 4 40 109,711 United Kingdom 22 1119 136 2


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1. shanewilson 5 20 140,005 Australia 35 2222 295 2

1. slipperycoconut 5 63 81,409 United States 16 1020 128 1

1. zento 10 34 117,15 Argentina 14 6032 207 2

Average 5.7 24.21 1411.86 146.93 2.43

2. christinasworld 9 96 61,117 United States 30 680 126 3

2. darkstar 6 Canada 14 480 160 7

2. foolsgold 5 73 73,728 United Kingdom 39 1150 201 4

2. gypsymichelle 4 Philippines 63 1299 225 25

2. jeffsadventures 7 5 239,234 United States 25 3268 215 1

2. off_road_clara 5 92 63,162 United Kingdom 20 1492 485 9

2. radsolv 6 United States 79 510 734 31

2. scottwoz 10 12 188000 United Kingdom 22 5558 582 3

2. will 9 Australia 34 1994 365 4


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2. yellowdragon 5 Germany 23 1647 133 10

Average 6.6 34,9 1807,8 322,6 9,7

3. exploreamerica 6 36 9962 406 17

3. jawad 7 10 203,191 United Kingdom 17 999 304 2

3. lucinate 6 16 159,106 Australia 51 3798 333 2

3. Paul 8 Thailand 20 3123 200 2

3. siscri 8 69 75,247 Ireland 28 487 128 5

3. twittg 6 23 133,082 United States 70 2706 355 9

3. uncledavros 7 3 417,829 Australia 63 14158 479 3

Average 6.8 40,71 5033,28 315 5,71

4. eddakath 14 30 121,849 Australia 14 9746 559 3

4. Jessica_cdn 9 Canada 24 218 164 9

4. Iraleigh 11 15 164,485 United States 34 4373 402 3


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4. peacefrog 11 11 188,367 France 48 5922 650 5

4. stevejames 12 4 266,808 United Kingdom 15 114 376 2

4. technotrekker 14 1 674,165 Australia 47 4477 325 2

Average 11.8 30,33 4141,66 412,66 4

5. lucky 14 53 85,875 Canada 43 1667 122 11

5. wakingdream 14 Canada 16 1061 82 6

Average 14 29,5 1364 102 8,5

6. thymeoff 16 6 226,049 Canada 24 2446 161 1

7. whereshegoes 30 2 536,275 Canada 38 2454 737 5

Table 1. Description of clusters of most linked travelers (visited 5 and 6/1/2009).


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4.1 Clusters characteristics

This section deals with the analysis of the characteristics of travelers

clusters. The seven clusters consist of the most linked travelers.

According to the methodology of cluster analysis, travelers within

each cluster are linked almost simultaneously by the travelers. So in

this way the travelers in a formed cluster may be regarded to be of

the same family have common characteristics - by travelers who

link them. A question that arises is on what basis other travelers link

to each group of central travelers? It is probable that common

characteristics of central travelers place them in the same cluster

according to other travelers choices (favorites). Thus it is interesting

to investigate whether blogs within clusters share common

properties. Several characteristics were taken into account in order to

see whether they may be associated with placement of travelers to the

specific clusters. They may not be all the characteristics necessary to

drive to a solid conclusion but rather they must be considered to

constitute an available set of data that allows making a first attempt

to analyze clusters profiles. These are traveler s number of

incoming links, his/her placement in the top100 TravelPod rating (in

case he/she belongs in this list), number of visitors to his/her site (on

6/1/2009), travelers country of origin, number of countries visited by

him/her, number of posted photos by him/her (till 5/1/2009), number

of posted entries (till 5/1/2009), number of his/her travel blogs

(5/1/2009), duration of his/her membership, and whether he/she

received TravelPod Badges (by means of recognition of his/her

achievements or history regarding participation in TravelPod, for

example if he/she is a founding member) (Table 1).


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Clusters with higher number of incoming links consist to a

higher degree of travelers with TravelPod budges (founding members

etc), longer period of membership and more posted entries in their

blogs. This can be verified by correlation coefficients calculated for

number of incoming links and the travelers characteristics (Table 2).

In addition, placement in TravelPod top100 list is correlated though

not significantly with number of incoming links. This means that

there is a tendency for those travelers who are ranked high (according

to visits to their sites) to be part of clusters with high number of

incoming links. However, the actual number of visitors is not

correlated with number of incoming links, so number of visits to their

own sites, does not provide evidence of the placement of travelers in

some cluster and therefore is not connected in this since with

networking patterns among travelers.

Regarding travelers country of origin, travelers mainly come

from Australia, United Kingdom, Canada and United States. Only

few come from Argentina, France, Germany, Ireland, Philippines,

and Thailand. Travelers that belong to most linked clusters, those are

Clusters 5, 6 and 7, and they all come from Canada. Regarding the

number of countries, that the travelers have visited there is not a

linear correlation between this number and the cluster membership

(Table 2). Regarding number of posted photos and posted entries of

the travelers, it is only number of entries which is significantly

correlated with number of incoming links, and cluster membership.

Travelers that are linked higher, also have higher communication

flow by means of how frequently they address to visitors of the

website by narrating their experience. On the contrary, number of


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blogs of the travelers is not correlated with number of incoming

links.

Finally, duration of membership and TravelPod budges are

correlated with number of incoming links. The most linked travels

have also entered TravelPod earlier and hold some budges that is

they are founding members or moderators, etc. In this sense, it seems

that duration of membership and TravelPod budge holding are

synonyms. To clarify the connection of the travelers profiles with

cluster membership, a stepwise regression analysis is performed,

considering number of incoming links as the dependent variable and

the rest variables as independent (Table 3). Only posted entries and

membership duration are affecting number of incoming links (and

consequently cluster membership). According to Beta absolute

values they affect number of incoming links nearly equally, although

posted entries present a higher coefficient (in absolute value). Highly

linked travelers have more posted entries and are members of

TravelPod for a longer time.

Number of incoming

links

Placement in Top100 TravelPod rating -.351

Number of visitors .031

Number of countries visited -.029

Posted photos .186

Posted entries .408(**)

Number of travel blogs the traveler has

created

-.108


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Member since (year) -.317(*)

TravelPod budges (1 yes/ 0 no) .379(*)

(**: p< 0.01, *: p< 0.05)

Table 2. Correlation coefficients of number of incoming links with


B Std. Error Beta T p

Constant 2771.280 1108.315 2.500 .020

Posted entries .018 .005 .559 3.340 .003

Member since (year) -1.381 .53 -.418 -2.497 .021

R2=0.403

Table 3. Stepwise regression of number of incoming links by


5. ConclusionsThe originality of the paper lies on the study of travelers

interconnections using multivariate statistics on data of social

networks to describe core travelers groups. By adopting ideas from

political blogging, the paper proposes a methodology for locating

linkage patterns and describes how travelers are networked, forming


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in this way central groups of travelers. By studying travelers,

interconnection the paper has shown that only few travelers are really

involved in networking, while the average interlinking is low.

However, there exist central travelers groups, which can be located

by using the proposed method. Other travelers within TravelPod.com

recognize and distinguish these groups possibly because travelers

within each group have common characteristics. Analysis provides

evidence that cluster-group membership is correlated with common

travelers characteristics. Also, travelers within some of the clusters-

groups are most active in posting entries while they are members of

Travelpod for a long time. In this sense, core groups contain the most

active and more information providing travelers. It is more likely for

these travelers to be reached by others who navigate through a series

of incoming links that lead to them. In this fashion, it is probable that

these travelers have the potential to address many visitors of the site

and therefore it is probable that they have a bigger impact on the

provision of information. Locating these travelers is important and

useful for both researchers and practitioners, since one can easily

locate them and be informed and analyze discussions of the most

influential travelers. Having in mind that Travelpod.com is one of the

top travel blogs sites, we might consider this case as indicative of the

relative sites.

Due to the unbiased information shared in blogs based on

first-hand authentic travel experiences, many travelers tend to use

and trust blogs information for searching travel information. Blogs

are becoming a very important information source for international

travelers for getting travel advice and suggestions. Blogs play an

increasingly important role in the consumer decision making process


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with the faceless reviewers who add their comments to these web-

pages rapidly becoming the travel opinion leaders of the electronic

age. Considering the importance of blogs perhaps the most useful

application of travel blogs for destination marketing organizations

and tourism businesses is monitoring the attitudes of travelers.

Identifying the characteristics of the authors and readers, and

assessing the extent to which these characteristics match those of key

markets is challenging. Understanding how influential specific blogs

are among their readers is a challenge. On these grounds the paper

strived to identify influential travelers with relevance to the impact

they have on other travelers and regarding to their trustworthiness

measured by the number of incoming links to them. Central

travelers blogs are the most popular and it has been shown from the

analysis, that they are most active in uploading posts, pictures and

information in general. For travelers who wish to be informed,

locating central travelers blogs means avoiding further navigation

through other blogs in order to find information. Thus, travelers who

wish to find information may limit their navigation for economy to

the most linked travelers blogs.

By locating central travelers, a researcher may understand

what the popular characteristics or content are. On the other hand,

knowing where and how people are commenting and sharing their

opinions about their brands gives companies the opportunity to

respond and influence their perceptions of them. Central travelers

could be really valuable to companies regarding provision of

information.

This research is limited to the use of a specific network, it

was performed at some specific time interval, by using a specific


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methodological approach. The study of other travelers networks by

using also other graph theoretic approaches would be useful to test or

enhance the findings.

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