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Towards a better understanding of the cognitive destination image of the Basque Country based on

the analysis of UGC

Ainhoa Serna Ph.D. Aurkene Alzua, Ph.D

Jon Kepa Gerrikagoitia, Ph.D

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Agenda

• Introduction– Problem

– Previous research

• Method

• Evaluation results

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IntroductionOpinions and UGC

• The opinions and experiences are central to almost all human activities and are key factors in influencing our behavior. Our beliefs and perceptions of reality, and the decisions we make, are largely conditioned by how others see and evaluate the world.

• When we have to make a decision often seek the opinions of others (Liu, 2012).

• Consumers no longer rely exclusively on official sources to obtain information about destinations and services. Visitors take into higher condideration UGC because those comments are based on experience (Pan et al., 2008) obtaining a more complete image of the destination and its products (Stepchenkova, 2013; Pang et al., 2011).

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IntroductionSocial Networks

• With the explosion of Web 2.0 consumers have a place where to share their experiences (Çakmak, 2012). Thus, users opinion gains weight against traditional marketing techniques (Ditoiu et al, 2012).

• Social networks are affecting very directly to brands and e-commerce. 65% of active Facebook accounts follow a brand (Llantada, 2013). Facebook reaches 85% of Internet users and Twitter 32%.

• Social networks support the complete cycle of tourism: inspiration, planning, booking, stay and content generation based on experiences at the destination.

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IntroductionSocial Networks

• Much of the UGC is generated by social networks like Twitter, Facebook, ... millions of users use them every day and this information is valuable for modeling the vision of the tourist destination.

• Volume of UGC (reviews, forums, blogs, twitter, comments, and posts on social networks) has reached astronomical proportions (Liu, 2012) making imposible the work of monitoring manually the opinions (Subrahmanian, 2009).

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IntroductionDestination Image

• The UGC and social networks become a valuable source to get insights about the image of the destination.

• The analysis of the discourses on the web provides an approach to extract information and knowledge associated with how destination is perceived, motivations, and underlying discourses.

• A proper management of perceptions or images that a potential visitor can have on a destination contributes to create a different positioning (Stepchenkova and Morrison, 2008), accordingly the destination image is presented as a key factor (Echtner and Richie, 1991, 1993; Chon, 1991).

• Despite the great importance of the destination image (Gartner, 1986, 1987), there is not a universally accepted and validated model (Crompton, 1977; Baloglu & McCleary, 1999a).

» Choosen reference model, Gómez, M. et al (2013).

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IntroductionAim

• Our aim is (what): • to model the cognitive destination image of the

Basque Country by visitors, • having as reference the conceptual model

proposed by Gomez, M. et al (2013)• based on data collected from relevant digital

media: destination sites, general social networks, traveler blogs, reviews, ... etc.

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IntroductionAim

• This work involves (how):• automatic semantic identification of

opinions from social networks • using a text mining tool adapted to the

domain of the destination,• developed by CICtourGUNE and

Mondragon Unibertsitatea.

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IntrodutionPrevious Research

• Destination image has been defined as an individual’s overall perception or total set of impressions of a place (Hunt, 1975; Phelps, 1986; Fakeye and Crompton, 1991)

• Research has been conducted since the early 1970s (Hunt, 1975; Mayo, 1973) and comprises hundreds of studies to date with several assement methods

– Most tourism scholars agree that destination image holds at least two distinctive components – cognitive (knowledge and beliefs) and affective (feelings towards a destination).

– Other authors proposed that destination images are formed by four interrelated components: cognitive, affective, evaluative and behavioural (Boulding, 1956).

– Echtner and Ritchie (2003) argue that a destination image is processed in a three dimensional space: namely functional, psychological and holistic.

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IntrodutionPrevious Research

• From the cognitive perspective (M.Gomez et al, 2013) purpose a destination image model based on 4 dimensions: Natural and Cultural Resources, Infrastructures and Socio-economic environment, Social constraints and atmosphere constraints

• The literature considers the amount and variety of secondary data sources as an external variable that contributes significantly to the formation of the image (Gartner & Hunt, 1987; Um & Crompton, 1990; Bojanic, 1991, Gartner, 1993; Font, 1997; Baloglu, 1999):

• (Stepchenkova , 2012) used UGC and social networks (Flickr) to create maps representing the projected and perceived image of a destination.

• (Andrade, 2010) uses destination websites to build the destination image by the tourist.

• (Xiang & Gretzel, 2010) have used the eWoM, specifically travel blogs, in the evaluation of a destination image.

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Methodology

• The model will be generated according to next steps:

@1. Defining and selecting relevant data @2. Extracting categories from the selected data sources @3. Transforming the categories into a model

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MethodologySources

3.1 Defining and selecting task relevant data– A prior analysis was carried out taking into account the

quality and quantity of the data from a double perspective, the projected image by the DMO and the perceived image by the visitor.

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MethodologySources

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MethodologyData Gathering

3.2 Extracting categories from the selected data sources•3.2.1 Data gathering

– The data extraction is an automatic process achieved by a software program

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MethodologyData Gathering

Different data gathering techniques are used depending on the source:

• Minube provides an API (Application Programme Interface) allowing searches at territory (Araba, Bizkaia, Gipuzkoa) and city (Bilbao, San Sebastián, Vitoria-Gasteiz) disaggregation level.

• Data from twitter si extracted using the Twitter4J API (Java library)

• Facebook is queried through the Facebook Graph API. • The remaining data sources are dumped using web

crawling and scraping techniques developed ad-hoc.

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MethodologyEntity recognition and categorization

3.2.2 Entity Categorization•The process of grouping entities into entity categories is called entity categorization.•First entities are extracted achiving the named entity recognition (NER) process (Hobbs and Riloff, 2010; Mooney and Bunescu, 2005; Sarawagi, 2008). •Once the recognized entities are classified a list of categories, entities and number of occurrences is obtained.

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MethodologyEntity recognition and categorization

• Enhanced and adapted instruments by the research group:• An ontology to obtain the categories. • A text mining tool based on the Wordnet lexical database that

implements an algorithm for learning generalized association rules (hierarchy for categorization and pairs for association)

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Methodology3.3 Transforming the categories into a modelMatching of the categories and subcategories with the four dimensions of the model Gomez, M et al. (2013) automatically. 1.The first dimension (REC) represents the natural and cultural resources (attractions) including nature, architecture, cultural attractions, customs and cultural activities. 2.The second dimension (INF) represents infrastructures and the socioeconomic environment: accommodation, entertainment, shopping, development and value for price. 3.The third dimension (SOC) represents Social Conditions: sustainability, cleaning, security, pollution ...4.The fourth dimension (ATM) represents the Atmosphere: relax, absence of mass, “peace and rest”.

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Evaluation Results

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Evaluation Results

• The resulting model allows us to approach the cognitive destination image of the tourist about the Basque Country based on the user-generated content about experiences related to basque territory.

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Evaluation ResultsMuseo Guggenheim

Torres Isozaki Playa de Sopelana

Bosque de Oma

Santuario de Urkiola

San JuanDe Gaztelugache

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Evaluation Results

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Evaluation Results

• Language: About 70% of the visitors in Euskadi are Spanish speakers, the sample of the elaborated discourse taken into account is almost completely in this language.

• The projected image in twitter and facebook uses the English to translate original messages in spanish. The projected image is fostered by embassadors who are residents and basque language (eu) appears in discourses.

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Evaluation Results

• TripAdvisor is the network with the most number of comments with little influence and presence of the DMO (Destination Management Organization) but spontaneous user-generated content, thus it is an important data source to know the perceived image of the destination.

• The DMO does an important effort of communication and promotion by Facebook and twitter.

• The type of discourse is mostly related to the source, by the type of messages or the constraints of the channel. The more elaborated discourses show more and better described entities.

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Evaluation Results

The categorization process obtains as results a table by data source with the identified entities, number of occurrences, and the corresponding category

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Evaluation Results

Twitter Euskadi Turismo i_Euskadi (TV Euskadi)

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Conclusions and Future lines• TripAdvisor is the social network with the most

number of purely user-generated comments. • TripAdvisor has little influence and presence of

the DMO. • The DMO does an important effort promoting the

destination using Facebook and twitter, having this activity a direct influence on the projected image.

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Conclusions and Future lines

• The type of discourse is related to the source, by the type of messages or the constraints of the channel. More elaborated discourses -> better described entities.

• All the analyzed sources have a major impact on the first dimension concerning natural and cultural resources.

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Conclusions and Future lines

• The DMO´s efforts are focused on the first dimension, everything related to resources such as Nature, Architecture, Cultural Attractions, Traditions, Cultural Activities.

• The broadcasting of the event agenda by the DMO has a big presence in facebook and twitter.

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Conclusions and Future lines

• The cultural attributes of the destination are relevant in the discourses.

• There is a significant interest in the subcategory of cultural activities: theater, exhibition, jazz, ... etc.

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Conclusions and Future lines

• The third and fourth dimensions may be considered in the communication strategy of the DMO.

• Within the natural and cultural resources, that gastronomy becomes the most relevant category by the quantity of related terms and concepts that appear especially in the user-generated content by visitors in all sources.

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Conclusions and Future lines

• The named entity recognition and categorization process provides many variables that are out of the scope of this paper and will be studied in future works, like the sentiment of the speech, the most important points of interest that are commented, attractiveness of a place,….

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Conclusions and Future lines

• To sum up, the analysis of the user-generated content using domain oriented text mining tools provides an approach to understand the destination in the way what people says and perceives and what DMO communicates.