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This paper aims to examine key parameters of scholarly context and geographic focus and provide an assessment of theoretical underpinnings of studies in the field of social media and visitor mobility. This review also summarised the characteristics of social media data, including how data are collected from different social media platforms and their advantages and limitations. The stocktake of research in this field was completed by examining technologies and applied methods that supported different research questions.

This literature review applied a mix of methods to conduct a literature review. This review analysed 82 journal articles on using social media to track visitors’ movements between 2014 and November 2020. The literature compared the different social media, discussed current applied theories, available technologies, analysed the current trend and provided advice for future directions.

This review provides a state-of-the-art assessment of the research to date on tourist mobility analysed using social media data. The diversity of scales (with a dominant focus on the city-scale), platforms and methods highlight that this field is emerging, but it also reflects the complexity of the tourism phenomenon. This review identified a lack of theory in this field, and it points to ongoing challenges in ensuring appropriate use of data (e.g. differentiating travellers from residents) and the ethics surrounding them.

The findings guide researchers, especially those with no computer science background, on the different types of approaches, data sources and methods available for tracking tourist mobility by harnessing social media. Depending on the particular research interest, different tools for processing and visualization are available.

利用社交媒体了解游客的流动性：信息技术和大数据的作用

本综述审查了学术背景的关键参数和案例调查的地理焦点, 并评估了社交媒体和访客流动领域研究的理论基础。本文章还总结了社交媒体数据的特征, 包括如何从不同社交媒体平台收集数据及其优势和局限。 此外本论文通过研究不同的应用方法和总结相关技术来完成的。

本研究提供了最新的使用社交媒体数据分析游客流动性研究的评估。比如案例分析的地理大小（主要集中在城市尺度）、社交媒体平台和方法的多样性突出了该领域的新兴, 但复杂旅游流动现象。审查发现利用社交媒体进行的研究缺乏理论贡献, 并指出在确保适当使用数据（例如区分旅行者与居民）和围绕他们的道德规范方面存在持续挑战。

研究结果指导研究人员, 尤其是那些没有计算机科学背景的研究人员, 了解不同类型的方法、数据来源和方法, 可用于通过利用社交媒体来跟踪旅游流动性。根据特定的研究兴趣, 可以使用不同的处理和可视化工具。

关键词：旅游模式; 游客流动; 游客轨迹; 社交媒体; 信息技术; 大数据

文章类型： 文献评论

Uso de las redes sociales para comprender la movilidad turística: el papel de la tecnología de la información y los macrodatos

En este estudio se examinan los parámetros clave en el contexto académico y enfoque geográfico y se evalúan los fundamentos teóricos de estudios en el campo de las redes sociales y la movilidad de los visitantes. Se resumen también las características de datos de las redes sociales, incluidos los métodos de recopilación de datos de las diferentes plataformas de redes sociales así como sus ventajas y limitaciones. Finalmente, se examinan tecnologías y métodos aplicados que respaldan las diferentes cuestiones de la investigación.

El estudio proporciona una evaluación avanzada del conocimiento hasta la fecha sobre la movilidad turística analizada utilizando datos de redes sociales. La diversidad de escalas (con un enfoque dominante en la escala de la ciudad), plataformas y métodos indica que este campo está en auge, pero también refleja la complejidad del fenómeno turístico. En este estudio se identifica una falta de teoría en este campo y se señalan los contúnios desafíos para garantizar el uso apropiado de datos (por ejemplo, diferenciar a los viajeros de los residentes) y la ética que los rodea.

los resultados guían a los investigadores, especialmente a aquellos sin formación en informática, sobre los diferentes tipos de enfoques, fuentes de datos y métodos disponibles para rastrear la movilidad turística mediante el uso de las redes sociales. Existen diferentes herramientas de procesamiento y visualización disponibles dependiendo del interés particular de la investigación.

Patrones de viaje; Movilidad turística; Movimientos de visitantes; Redes sociales; Tecnologías de la información; Macrodatos.

This study aims to extend the study of COVID-19 effects by identifying different consumer behaviors beyond panic buying during the pandemic.

A systematic review was based on Herjanto et al.’s (2020a) thematic analysis and Paul et al.’s (2021) 5W1H framework, and the authors analyzed 52 related papers.

The result findings indicated that during the COVID-19 pandemic, consumers fell into five different consumer behavior categories: health-related behaviors, consumption behaviors, ethical behaviors, behavioral intentions, and other related behaviors, and social connectedness behaviors. Findings show that consumer behaviors were increasingly complex and dynamic during the pandemic.

This systematic review will provide significant contributions to academia by offering general and technical insights and to practitioners by presenting guidelines on dealing with such different behaviors.

Destination monitoring is crucial to understand performance and identify key points of differentiation. Visitor satisfaction is an essential driver of destination performance. With the fast-growing volume of user-generated content through social media, it is now possible to tap into very large amounts of data provided by travellers as they share their experiences. Analysing these data for consumer sentiment has become attractive for destinations and companies. The idea of drawing on social media sentiment for satisfaction monitoring aligns well with the broader move towards smart destinations and real-time information processing. Thus, this paper aims to examine whether the electronic word of mouth originating from Twitter posts offers a useful source for assessing destination sentiment. Importantly, this research examines what caveats need to be considered when interpreting the findings.

This research focusses on a prominent tourist destination situated on Australia’s East Coast, the Gold Coast. Using a geographically informed filtering process, a collection of tweets posted from within the Gold Coast destination was created and analysed. Metadata were analysed to assess the population of Twitter users, and sentiment analysis, using the Valence Aware Dictionary for Sentiment Reasoning algorithm, was performed.

Twitter posts provide considerable information, including about who is visiting and what sentiment visitors and residents express when sending tweets from a destination. They also uncover some challenges, including the “noise” of Twitter data and the fact that users are not representative of the broader population, in particular for international visitors.

This paper highlights limitations such as lack of representativeness of the Twitter data, positive bias and the generic nature of many tweets. Suggestions for how to improve the analysis and value of tweets as a data source are made.

This paper contributes to understanding the value of non-traditional data sources for destination monitoring, in particular by highlighting some of the pitfalls of using information sources, such as Twitter. Further research steps have been identified, especially with a view to improving target-specific sentiment scores and the future employment of big-data approaches that involve integrating multiple data sources for destination performance monitoring.

The identification of cost-effective ways of measuring and monitoring guest satisfaction can lead to improvements in destination management. This in turn will enhance customer experience and possibly even resident satisfaction. The social benefits, especially at times of considerable visitation pressure, can be important.

The use of Twitter data for the monitoring of visitor sentiment at tourist destinations is novel, and the analysis presented here provides unique insights into the potential, but also the caveats, of developing new, smart systems for tourism.

目的地监控对理解绩效和确立区别关键点至关重要。游客满意是目的地绩效的关键动力。随着社交媒体上用户生成内容的快速增长, 研究其游客提供的大量数据变成可能, 这些数据体现了游客的旅游体验。分析这些消费者情绪的数据对目的地和有关企业的吸引力巨大。研究社交媒介情绪数据和满意度与更广泛地对智慧旅游和实时信息处理等方面的研究和谐一致。因此, 本论文旨在检验Twitter帖子中的在线口碑效应是否成为测量目的地情绪的有用数据。更重要的是, 本论文检验在研究结果中哪些领域应该着重考虑研究。

本论文集中研究了澳大利亚东海岸的一处旅游目的地, 黄金海岸。本论文使用地理信息过滤的处理方式, 有关黄金海岸的tweets为样本, 进行分析。本论文分析了元数据, 使用VADER数算, 检测了Twitter用户人口和情绪分析。

Twitter帖子提供相当多的信息, 包括谁是游客, 当游客发布有关旅游目的地的tweets的时候, 拥有什么样的情绪。研究结果还指出了一些挑战, 包括twitter数据的“杂音”, 用户并不能代表广大研究对象的事实, 特别是国际游客。

本论文强调了几点限制, 如Twitter数据的代表性、积极偏见、大多数tweets千篇一律等。本论文对如何提高分析结果和使用tweets作为数据源的价值提出了几点建议。

本论文对非传统数据以对旅游目的地监控的价值做出贡献, 尤其是强调了使用信息数据的弊端, 如Twitter。未来研究方向应该着重研究目标明确的情绪指数, 以及运用大数据分析方法, 分析多个数据源来检测旅游目的地性能。

本论文确立的经济有效的方法以衡量和监控游客满意度, 对提高目的地管理有着巨大帮助。同时, 这也可以提高游客体验和甚至提高当地居民的满意度。社会利益, 特别有的时候很大的旅游压力, 是巨大的。

COVID-19 disrupted the lives of consumers across the globe, and the retail sector has been one of the hardest hits. The impact of COVID-19 on consumers' retail choice behaviour and retailers' responses has been studied in detail through multiple lenses. Now that the effect of COVID-19 is abating, there is a need to consolidate the learnings during the lifecycle of COVID-19 and set the agenda for research post-COVID-19.

Scopus database was searched to cull out academic papers published between March 2020 and June 6, 2022, using keywords; shopping behaviour, retailing, consumer behaviour, and retail channel choice along with COVID-19 (171 journals, 357 articles). Bibliometric analysis followed by selective content analysis was conducted.

COVID-19 was a black swan event that impacted consumers' psychology, leading to reversible and irreversible changes in retail consumer behaviour worldwide. Research on changes in consumer behaviour and consumption patterns has been mapped to the different stages of the COVID-19 lifecycle. Relevant research questions and potential theoretical lenses have been proposed for further studies.

This paper collates, classifies and organizes the extant research in retail from the onset of the COVID-19 pandemic. It identifies three retail consumption themes: short-term, long-term reversible and long-term irreversible changes. Research agenda related to the retailer and consumer behaviour is identified; for each of the three categories, facilitating the extraction of pertinent research questions for post-COVID-19 studies.