Community crosstalk: an exploratory analysis of destination and festival eWOM on Twitter

Williams, Nigel; Inversini, Alessandro; Buhalis, Dimitrios; Ferdinand, Nicole

doi:10.1080/0267257x.2015.1035308

Cited by 59 publications

(65 citation statements)

References 121 publications

(59 reference statements)

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“…Social media can generate electronic versions of traditional word-of-mouth, and often consist of comments published by travelers on the tourism products/services they experience (Filieri;Mcleay, 2014, Williams, Inversini, Buhalis, Ferdinand, 2015. Also, there is an awareness among tourism managers that social media content and reviews form a rich source of data (Phillips;Zigan;Silva;Schegg, 2015).…”

Section: Introductionmentioning

confidence: 99%

Content mining framework in social media: A FIFA world cup 2014 case analysis

Thomaz

Biz

Bettoni³

et al. 2017

Information & Management

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Evaluation; Results Visualization.. The framework was tested empirically during the FIFA World Cup 2014 at Curitiba (Brazil) as one of the main host city destinations. The research focused on mining of Twitter content with tourist services ontology (hospitality, food & beverages and transportation). In total 58,686 valid messages were collected, analyzed and associated with an application ontology. Content analysis demonstrated an accurate, real time reflection of tourism services. The framework is effective to collect relevant content and identify popular topics in social media towards strategic and operational tourism management. A better understanding of dynamic conversations facilitates better and faster decision making and a rapid response in real time that can support tourism organization and destination competitiveness.

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Section: Introductionmentioning

confidence: 99%

Content mining framework in social media: A FIFA world cup 2014 case analysis

Thomaz

Biz

Bettoni³

et al. 2017

Information & Management

Self Cite

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“…But going beyond those ideas, the study highlights the need to analyse the explicit actors, typologies and components that stand out in a specific social media platform, given their variety. In this sense, it would be interesting to complete this study with a future comparison with other platforms, such as Facebook (Kim, Kim, & Wise, 2014) or Twitter (Williams, Inversini, Buhalis, & Ferdinand, 2015), where the presence of DMOs plus traditional media and other third parties is increasing. Here, the image typology will vary widely, probably mixing organic elements with autonomous and induced and reflecting the prevalence of non-solicited, affective and conative images projected by the demand-side.…”

Section: Discussionmentioning

confidence: 98%

Barcelona seen through the eyes of TripAdvisor: actors, typologies and components of destination image in social media platforms

Tamajón

Valiente

2015

Current Issues in Tourism

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Recent literature has emphasized the emerging role of social media in generating a destination image. Even so, there is still no clear definition of the main actors, typologies and components prevailing in these environments or the possible differences between diverse social platforms. Specific analysis and comparison of the Barcelona TripAdvisor Forum and a traditional Destination Management Organization (DMO) website showed that actors, typologies and components are platform specific and have both similarities and differences with other sources, including DMO websites and other social media platforms. Introduction and literatureResearch on the processes of constructing a destination image began with Gunn's (1972) formulation of the 'dimorphic theory', indicating that the image of a destination is formed through a process where two types of actors generate two image typologies: tourists have their own impression (organic image) and are exposed to information from other sources as Destination Management Organizations (DMOs) promotions, media interpretations and 'word of mouth' (induced image). Based on this theory, Gartner (1993) identified different actors generating eight sub-typologies through different sources: overt induced I (institutions, through traditional forms of advertising), overt induced II (travel intermediaries, through requested information), covert induced I (celebrity spokespersons, through recommendations), covert induced II (specialists, through reports, stories or articles), autonomous (various actors, through independent information such as news and movies), unsolicited organic (friends, through unsought information), solicited organic (friends, through word of mouth) and organic (personal travel experience). Moreover, Gartner believed that these sources have an impact on two different but hierarchically interrelated image components: cognitive, concerning the understanding and evaluation of a known product, and affective, involving motives and feelings that an individual has for selecting a destination. The third potential 'action' component is conative, driving the ways that a person acts on those thoughts and feelings.

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“…In this study, SNA is used for qualitative research. "The social science approach to SNA attempts to develop a qualitative understanding of node and network properties" (Williams et al, 2015). In this study, SNA is used for:…”

Section: Social Network Analysis (Sna)mentioning

confidence: 99%