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This paper aims to track how ecotourism has been presented in a digital world over time using geotagged photographs and internet search data. Ecotourism photographs and Google Trends search data are used to evaluate tourist perceptions of ecotourism by developing a categorization of essential attributes, examining the relation of ecotourism and sustainable development, and measuring the popularity of the ecotourism sites.

The researchers collected geotagged photographs from Flickr.com and downloaded Google search data from Google Trends. An integrative approach of content, trend and spatial analysis was applied to develop ecotourism categories and investigate tourist perceptions of ecotourism. First, the authors investigate ecotourism geotagged photographs on a social media to comprehend tourist perceptions of ecotourism by developing a categorization of key ecotourism attributes and measuring the popularity of the ecotourism sites. Second, they examined how ecotourism has been related with sustainable development using internet search data and investigate the trends in search data. Third, spatial analysis using GIS maps was used to visualize the spatial-temporal changes of photographs and tourist views throughout the world.

This study identified three primary themes of ecotourism perceptions and 13 categories of ecotourism attributes. Interest over time about ecotourism was mostly presented as its definitions in Google Trends. The result indicates that tracked ecotourism locations and tourist footprints are not congruent with the popular regions of ecotourism Google search.

This research follows the changing trends in ecotourism over a decade using geotagged photographs and internet search data. The evaluation of the global ecotourism trend provides important insights for global sustainable tourism development and actual tourist perception. Analyzing the trend of ecotourism is a strategic approach to assess the achievement of UN sustainable development goals. Factual perspectives and insights into how tourists are likely to seek and perceive natural attractions are valuable for a range of audiences, such as tourism industries and governments.

研究目的本论文旨在探索生态旅游业在电子世界中是如何随着时间而显示出来的，文章样本为带有地理标记的图片和互联网搜索数据。本文使用生态旅游图片和谷歌趋势搜索数据来评估游客对生态旅游的感知，通过对关键要素的分类，审视生态旅游和可持续发展的关系，以及衡量生态旅游基地的受欢迎程度等方法。

本论文作者从Flickr.com上搜集地理标记图片以及从谷歌趋势上下载谷歌搜索数据。样本分析通过内容、趋势、空间上的综合分析，来开发生态旅游类别和游客对生态旅游的感知。首先，我们研究了社交媒体上的生态旅游地理标记图片以理解游客对生态旅游的感知情况，以此搭建了关键生态旅游要素的类别体系，和衡量生态旅游基地的受欢迎程度。第二，我们通过使用互联网搜索数据，检测了生态旅游如何与可持续发展相连接，以及研究了搜索数据中的趋势。第三，我们使用了GIS软件来操作空间分析，对图片的空间-时间改变和游客对世界的观点做了可视化处理。

本论文确立了三项生态旅游感知的基本主题以及13项生态旅游要素类别。生态旅游互联网随着时间演化，根据谷歌趋势上的定义，被大致地展现出来。本论文研究结果表示生态旅游地理位置和游客足迹与生态旅游谷歌搜索的热门区域不全是完全吻合的。

本论文使用地理标记图片和互联网搜索数据将生态旅游发展趋势近十年的变化描画出来。全球生态旅游趋势的评估对全球可持续旅游发展和实际游客感知方面做出重要见解启示。生态旅游趋势的分析作为一种战略方法，对UN可持续发展目标的时间起到评估作用。本论文针对游客的真实感知和意见，游客如何选择和感知自然景观，这对于很多群体，比如旅游行业和政府，都有着重要意义。

The purpose of this paper is to provide a review of hospitality and tourism studies that have used social media analytics to collect, examine, summarize and interpret “big data” derived from social media. It proposes improved approaches by documenting past and current analytic practice addressed by the selected studies in social media analytics.

Studies from the past 18 years were identified and collected from five international electronic bibliographic databases. Social media analytics-related terms and keywords in the titles, keywords or abstracts were used to identify relevant articles. Book chapters, conference papers and articles not written in English were excluded from analysis. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guided the search, and Stieglitz and Dang-Xuan’s (2013) social media analytics framework was adapted to categorize methods reported in each article.

The research purpose of each study was identified and categorized to better understand the questions social media analytics were being used to address, as well as the frequency of each method’s use. Since 2014, rapid growth of social media analytics was observed, along with an expanded use of multiple analytic methods, including accuracy testing. These factors suggest an increased commitment to and competency in conducting comprehensive and robust social media data analyses. Improved use of methods such as social network analysis, comparative analysis and trend analysis is recommended. Consumer-review networks and social networking sites were the main social media platforms from which data were gathered; simultaneous analysis of multi-platform/sources of data is recommended to improve validity and comprehensive understanding.

This is the first systematic literature review of the application of social media analytics in hospitality and tourism research. The study highlights advancements in social media analytics and recommends an expansion of approaches; common analytical methods such as text analysis and sentiment analysis should be supplemented by infrequently used approaches such as comparative analysis and spatial analysis.

本文对酒店旅游学科中采用社交媒体数据分析的文献进行梳理。本文通过审阅其相关分析方法的文献来提出分析方法的改进策略。

样本数据包括过去18年中五个国际在线文献索引库中的文献。搜索通过标题、关键词、或者摘要中出现社交媒体数据分析等相关字样的文章。书章节、会议文章、以及非英文文章未被收录在索引中。系统回顾和文献综述的方法（PRISMA）指导本文文献索引, Stieglitz和Dang-Xuan（2013）社交媒体数据分析框架作为本文文献分类的方法。

本文汇报了每篇文献的研究目的以及系统归类以更好理解社交媒体数据分析的研究问题以及每种方法的使用频率。自2014年起, 社交媒体数据分析快速增长, 以及其他相关分析方法, 包括精度测试（accuracy testing）。这些结果表明更多全面、稳定的分析方法需求增强以及竞争激烈。本文推荐使用改良方法, 比如社交网络分析法、比较分析、趋势分析等。消费者评价网络和社交网站成为主要社交媒体网络数据的提供平台。本文推荐多源数据应该同步分析以提高有效性和全面性的理解。

本文是首篇酒店旅游领域中对社交媒体数据分析的系统文献回顾型文章。本文强调了社交媒体数据分析的先进性以及扩展其方法的全面性；常见分析方法比如文本分析和情感分析应该结合非常见的分析方法比如比较分析法和空间分析法进行系统分析。

关键词 – 关键词 对比分析, 情感分析, 用户原创内容,社交媒体分析, 主题模型, 空间分析, 文本分析文章类型 文献综述

The purpose of this study is to compare the spatial distribution of Airbnb and hotels in London and examine the relationship between demographic, socioeconomic and environmental factors and the supply of these two types of lodging supply.

Local information of Airbnb listings in London was collected through Insideairbnb.com. Gathered data were examined using geo-spatial auto-correlation analysis and spatial econometric models.

The results indicate that Airbnb predominates in the areas around popular tourist attractions and the peripheral areas of the city, while in the downtown area Airbnb and hotels are in the state of coexistence. The mono-centric model and the agglomeration model could be extended to the context of peer-to-peer accommodation. The location of Airbnb and traditional hotels capitalizes on different factors.

The study is based on secondary data due to data availability. And, it is based on the case of London, so the findings may not reflect the situation of small cities and rural destinations.

This study not only gives suggestions for local councils to regulate the location of hotels and Airbnb but also provides professional landlords with reference to choosing Airbnb location.

This study extends the hotel location theoretical models into the context of Airbnb and sheds lights on the distinction between these two business models in terms of location factors.

爱彼迎和酒店的位置：空间分布和关系

本研究旨在比较伦敦Airbnb和酒店的空间分布, 并探讨人口、社会经济和环境因素与这两类住宿供给的关系。

伦敦Airbnb房源信息通过Insideairbnb.com网站收集, 采用地理空间自相关分析和空间经济计量模型进行分析。

结果表明Airbnb主要集中在旅游景点周边和城市周边地区, 在市中心地区, Airbnb与酒店处于共存状态。单中心模型和聚集模型适用于共享住宿情境。Airbnb和传统酒店的空间分布影响因素的重要性存在差异。

本研究将酒店区位理论模型扩展到Airbnb情境下, 并揭示了这两种商业模式在区位因素方面的区别。

本研究采用二手数据。案例地为伦敦, 因此研究结果可能无法反映小城市和乡村旅游目的地的情况。

地方政府应更加重视专业公司的劳动力保障并限制旅游景点周边的短期租赁。

Ubicación de los hoteles y preferencias: distribución espacial y relaciones

El propósito de este estudio es comparar la distribución espacial de Airbnb y los hoteles en Londres y examinar la relación entre los factores demográficos, socioeconómicos y ambientales y la oferta de estos dos tipos de oferta de alojamiento.

La información local de los anuncios de Airbnb en Londres se recopiló a través de Insideairbnb.com. Los datos recopilados se examinaron mediante análisis de autocorrelación geoespacial y modelos econométricos espaciales.

Los resultados indican que Airbnb predomina en las zonas aledañas a los atractivos turísticos populares y las zonas periféricas de la ciudad, mientras que en la zona centro Airbnb y los hoteles se encuentran en estado de convivencia. El modelo monocéntrico y el modelo de aglomeración podrían extenderse al contexto de la acomodación entre pares. La ubicación de Airbnb y los hoteles tradicionales se basa en diferentes factores.

Este estudio amplía los modelos teóricos de ubicación de hoteles en el contexto de Airbnb y arroja luz sobre la distinción entre estos dos modelos comerciales en términos de factores de ubicación.

El estudio se basa en datos secundarios debido a la disponibilidad de datos. Y se basa en el caso de Londres, por lo que los hallazgos pueden no reflejar la situación de las ciudades pequeñas y los destinos rurales.

Los hallazgos sugieren que las autoridades locales deberían prestar más atención a la seguridad laboral de las empresas profesionales y restringir los alquileres a corto plazo alrededor de las atracciones turísticas.