Big data analytics of online news to explore destination image using a comprehensive deep-learning approach: a case from Mexico

IF 6.3 3区 管理学 Q1 HOSPITALITY, LEISURE, SPORT & TOURISM
Rafael Guerrero-Rodríguez, Miguel Á. Álvarez-Carmona, Ramón Aranda, Ángel Díaz-Pacheco
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Abstract

Destination image has been a subject of great interest to tourism scholars for several decades. Since the nature of this social construct is highly dynamic, its study poses new challenges under the current conditions of contemporary tourism practices. Considering that the image formation process can be influenced positively or negatively by multiple sources of information available to individuals, it is surprising that analyses of autonomous formation agents, such as online news, have received limited attention in related literature. Although existing studies have explored the influence of this information on image formation, intention to visit, and actual behavior, these normally adopt traditional methodologies to collect information, circumscribing the analysis to limited samples. The main objective of this work is to propose an innovative automated approach based on deep learning aimed at collecting and analyzing available textual data on the internet, such as online news, to produce a more comprehensive picture of the destination image in these sources of information. In order to test this approach, a destination from the country of Mexico was selected as a case study: Cancun. Given that the USA and Canada represent almost 60 percent of all international visitors to Mexico, the information search focused on this geographical context. A total of 3845 online news making reference to Cancun were retrieved during an entire year (July 2021–2022). The analysis of this information allowed the identification of recurrent topics covered by the media in both countries regarding destination safety issues, criminal activities, and the evolution of travel restrictions due to the COVID-19 pandemic. In addition to these topics, favorable coverage could also be detected including topics such as existing amenities in all-inclusive resorts as well as the recognition of Cancun as an ideal tourist destination for the international traveler. In practical terms, we believe this information can be useful for local government and DMOs to explore the evolution of the destination’s image as well as to identify sensitive issues covered in the media that require the implementation of communication strategies to counteract any potential negative effect. Finally, the proposed approach effectively contributes to making the tasks of destination image evaluation easier and faster than traditional research strategies.

Abstract Image

利用综合深度学习方法对在线新闻进行大数据分析,探索目的地形象:墨西哥案例
几十年来,目的地形象一直是旅游学者非常感兴趣的课题。由于这种社会结构的性质是高度动态的,因此在当代旅游实践的当前条件下,对其研究提出了新的挑战。考虑到图像形成过程可能受到个人可获得的多种信息来源的积极或消极影响,令人惊讶的是,对自主形成代理(如在线新闻)的分析在相关文献中受到的关注有限。虽然现有的研究已经探讨了这些信息对形象形成、访问意向和实际行为的影响,但这些研究通常采用传统的方法来收集信息,将分析局限于有限的样本。这项工作的主要目标是提出一种基于深度学习的创新自动化方法,旨在收集和分析互联网上可用的文本数据,例如在线新闻,以便在这些信息源中生成更全面的目标图像。为了测试这种方法,我们选择了墨西哥的一个目的地作为案例研究:坎昆。考虑到美国和加拿大占到墨西哥所有国际游客的近60%,信息搜索集中在这一地理背景上。在整个一年中(2021-2022年7月),共检索到3845条与坎昆有关的在线新闻。通过对这些信息的分析,确定了两国媒体经常报道的主题,包括目的地安全问题、犯罪活动以及COVID-19大流行导致的旅行限制的演变。除了这些话题,还可以发现有利的覆盖范围,包括诸如全包式度假村的现有设施以及坎昆作为国际旅行者理想旅游目的地的认可等话题。在实践中,我们相信这些信息可以帮助地方政府和dmo探索目的地形象的演变,以及识别媒体报道的敏感问题,这些问题需要实施传播策略来抵消任何潜在的负面影响。最后,与传统的研究策略相比,该方法有效地简化了目标图像的评估任务。
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来源期刊
Information Technology & Tourism
Information Technology & Tourism HOSPITALITY, LEISURE, SPORT & TOURISM-
CiteScore
18.10
自引率
5.40%
发文量
22
期刊介绍: Information Technology & Tourism stands as the pioneer interdisciplinary journal dedicated to exploring the essence and impact of digital technology in tourism, travel, and hospitality. It delves into challenges emerging at the crossroads of IT and the domains of tourism, travel, and hospitality, embracing perspectives from both technical and social sciences. The journal covers a broad spectrum of topics, including but not limited to the development, adoption, use, management, and governance of digital technology. It supports both theory-focused research and studies with direct relevance to the industry.
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