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This paper aims to conduct a review of the literature published, between 2006 and 2018, that used search engine data on tourism and hospitality research, namely, Google Insights for Search and Google Trends. More specifically, it intends to identify the purpose and context of the data use, ascertaining the main findings and reviewing the methodological approaches.

A systematic literature review of Scopus indexed research has been carried out. Given the novelty of search engine data use in tourism and hospitality research and the relatively low number of search results in Scopus, other databases were used to broaden the scope of analysis, namely, EBSCO and Google Scholar. The papers selected were subjected to content and statistical analyses.

Google Trends data use in tourism and hospitality research has increased significantly from 2012 to 2017, mainly for tourism forecasting/nowcasting; knowing the interest of users’ searches for tourist attractions or destinations; showing the relationship between the official tourism statistics and the search volume index of Google Trends; and estimating the effect of one event on tourism demand. The categories and search terms used vary with the purpose of the study; however, they mostly focus on the travel category and use the country as the search term.

Google Trends has been increasingly used in research publications in tourism and hospitality, but the range of its applications and methods used has not yet been reviewed. Therefore, a systematic review of the existing literature increases awareness of its potential uses in tourism and hospitality research and facilitates a better understanding of its strengths and weaknesses as a research tool.

本文回顾2006年至2018年发表文献使用酒店旅游相关的搜索引擎数据, 即Google Insights for Search 以及Google Trends。确切地说, 本文旨在研究数据使用目的和背景, 归纳主要研究成果和研究方法。

本文采用Scopus索引, 由于旅游酒店领域使用搜索引擎数据的文献较少, Scopus搜索结果样本量较低, 本文扩展到其他数据库, 即EBSCO以及Google Scholar。选定的样本文献采用文本分析和统计分析法。

旅游酒店领域中对Google Trends数据使用的增加主要集中在2012年到2017年, 主要研究领域有（1）旅游预测/即时预报；（2）了解用户搜索旅游景点或目的地的需求；（3）官方旅游数据和Google Trends搜索量索引之间的关系；以及（4）评估大事件对旅游需求的影响。文献归类和搜索名词根据研究目的而不同。然而, 大多数文章使用‘旅游’归类以及使用国家作为搜索关键词。

Google Trends在酒店旅游领域研究中的使用逐渐增加, 但是据作者所知, 其应用的范畴和方法仍处在起步阶段。因此, 对现有文献的系统回顾可以提高对其在旅游酒店领域中应用的认知, 并且本文结果使其作为研究工具的优劣分析更深理解。

Google Trends, Google insights for search, 搜索引擎数据, 旅游酒店研究, 系统文献回顾

The purpose of this paper is to examine internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices.

This paper examines internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices.

The empirical results show that all models augmented with Google data, combining both macro and search data, significantly outperform baseline models which abandon internet search data. Models based on Google data alone, outperform the baseline models in all cases. The models achieve a reduction over the baseline models of the mean squared forecasting error for transactions and prices of up to 35 and 54 per cent, respectively.

The results suggest that Google data can serve as an early market indicator. The findings of this study suggest that the inclusion of Google search data in forecasting models can improve forecast accuracy significantly. This implies that commercial real estate forecasters should consider incorporating this free and timely data set into their market forecasts or when performing plausibility checks for future investment decisions.

This is the first paper applying Google search query data to the commercial real estate sector.

The purpose of this paper is to analyze the effect of investor sentiment, measured with Google internet search data, on volatility forecasts of the US REIT market.

The author uses the S&P US REIT index and collects search volume data from Google Trends for all US REIT. Two different Generalized Autoregressive Conditional Heteroskedastic models are then estimated, namely, the baseline model and the Google augmented model. Using these models, one-step-ahead forecasts are conducted and the forecast accuracies of both models are subsequently compared.

The empirical results reveal that search volume data can be used to predict volatility on the REIT market. Especially in periods of high volatility, Google augmented models outperform the baseline model.

The results imply that Google data can be used on the REIT market as a market indicator. Investors could use Google as an early warning system, especially in periods of high volatility.

This is the first paper to use Google search query data for volatility forecasts of the REIT market.

The purpose of this paper is to examine the feasibility of discovering business information from search engine query data. Specifically the study tried to determine whether search volumes of company names are correlated with the companies’ business performance and position data.

The top 50 US companies in the 2012 Fortune 500 list were included in the study. The following business performance and position data were collected: revenues, profits, assets, stockholders’ equity, profits as a percentage of revenues, and profits as a percentage of assets. Data on the search volumes of the company names were collected from Google Trends, which is based on search queries users enter into Google. Google Trends data were collected in the two scenarios of worldwide searches and US searches.

The study found significant correlations between search volume data and business performance and position data, suggesting that search engine query data can be used to discover business information. Google Trends’ worldwide search data were better than the US domestic search data for this purpose.

The study is limited to only one country and to one year of data.

Publicly available search engine query data such as those from Google Trends can be used to estimate business performance and position data which are not always publicly available. Search engine query data are timelier than business data.

This is the first study to establish a relationship between search engine query data and business performance and position data.

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。

An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science).

The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials.

The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches.

The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.

Recent research has found significant relationships between internet search volume and real estate markets. This paper aims to examine whether Google search volume data can serve as a leading sentiment indicator and are able to predict turning points in the US housing market. One of the main objectives is to find a model based on internet search interest that generates reliable real-time forecasts.

Starting from seven individual real-estate-related Google search volume indices, a multivariate probit model is derived by following a selection procedure. The best model is then tested for its in- and out-of-sample forecasting ability.

The results show that the model predicts the direction of monthly price changes correctly, with over 89 per cent in-sample and just above 88 per cent in one to four-month out-of-sample forecasts. The out-of-sample tests demonstrate that although the Google model is not always accurate in terms of timing, the signals are always correct when it comes to foreseeing an upcoming turning point. Thus, as signals are generated up to six months early, it functions as a satisfactory and timely indicator of future house price changes.

The results suggest that Google data can serve as an early market indicator and that the application of this data set in binary forecasting models can produce useful predictions of changes in upward and downward movements of US house prices, as measured by the Case–Shiller 20-City House Price Index. This implies that real estate forecasters, economists and policymakers should consider incorporating this free and very current data set into their market forecasts or when performing plausibility checks for future investment decisions.

This is the first paper to apply Google search query data as a sentiment indicator in binary forecasting models to predict turning points in the housing market.

The purpose of this paper is to use data mined from Google Trends, in order to predict the unemployment rate prevailing among Canadians between 25 and 44 years of age.

Based on a theoretical framework, this study argues that the intensity of online leisure activities is likely to improve the predictive power of unemployment forecasting models.

Mining the corresponding data from Google Trends, the analysis indicates that prediction models including variables which reflect online leisure activities outperform those solely based on the intensity of online job search. The paper also outlines the most propitious ways of mining data from Google Trends. The implications for research and policy are discussed.

This paper, for the first time, augments the forecasting models with data on the intensity of online leisure activities, in order to predict the Canadian unemployment rate.

The purpose of this paper is to investigate the usefulness of search query volume data in forecasting demand for hotel rooms and identify the best econometric forecasting model.

The authors used search volume data on five related queries to predict demand for hotel rooms in a specific tourist city and employed three ARMA family models and their ARMAX counterparts to evaluate the usefulness of these data. The authors also evaluated three widely used causal econometric models – ADL, TVP, and VAR – for comparison.

All three ARMAX models consistently outperformed their ARMA counterparts, validating the value of search volume data in facilitating the accurate prediction of demand for hotel rooms. When the three causal econometric models were included for forecasting competition, the ARX model produced the most accurate forecasts, suggesting its usefulness in forecasting demand for hotel rooms.

To demonstrate the usefulness of this data type, the authors focused on one tourist city with five specific tourist‐related queries. Future studies could focus on other aspects of tourist consumption and on more destinations, using a larger number of queries to increase accuracy.

Search volume data are an early indicator of travelers' interest and could be used to predict various types of tourist consumption and activities, such as hotel occupancy, spending, and event attendance.

The paper's findings validate the value of search query volume data in predicting hotel room demand, and the paper is the first of its kind in the field of tourism and hospitality research.

The application of “Google” econometrics (Geco) has evolved rapidly in recent years and can be applied in various fields of research. Based on accepted theories in existing economic literature, this paper seeks to contribute to the innovative use of research on Google search query data to provide a new innovative to property research.

In this study, existing data from Google Insights for Search (GI4S) is extended into a new potential source of consumer sentiment data based on visits to a commonly‐used UK online real‐estate agent platform (Rightmove.co.uk). In order to contribute to knowledge about the use of Geco's black box, namely the unknown sampling population and the specific search queries influencing the variables, the GI4S series are compared to direct web navigation.

The main finding from this study is that GI4S data produce immediate real‐time results with a high level of reliability in explaining the future volume of transactions and house prices in comparison to the direct website data. Furthermore, the results reveal that the number of visits to Rightmove.co.uk is driven by GI4S data and vice versa, and indeed without a contemporaneous relationship.

This study contributes to the new emerging and innovative field of research involving search engine data. It also contributes to the knowledge base about the increasing use of online consumer data in economic research in property markets.