In recent years Internet search engines and social media have become important in the way people inform themselves. When people type in queries on search engines or use search query trough social media they implicitly express their interest in the object of the query. This thesis provides a framework for predicting Tourism demands using Google trends and twitter.
As we know that Tourism is a growing industry, according to the Organization for Economic Cooperation and Development, OESO (2016). It demonstrates that the tourism industry is now responsible for 9 % of the domestic product and employment worldwide.
Also in the Netherlands, the tourism industry is growing, as is its contribution to the Dutch economy (CBS, Trends rapport toerisme, recreatie en vrije tijd 2016, 2016). we choose Netherland because we know that The Netherlands is in northwestern Europe, is known for a flat landscape of canals, tulip fields, windmills and cycling routes, therefore it attracts many tourists from whole the world [1]
The biggest contribution to the Dutch economy by the tourism industry derives from tourism in Amsterdam (ING Economisch Bureau, 2016).
With over thirteen million night passes, Amsterdam is responsible for 30 % of the total number of night passes in the Netherlands and is the biggest touristic hotspot in the Netherlands for both business- and touristic guests.
In particular, search query data provide valuable information about tourists’ intention, interests, and opinions. Tourists use search engines and social media to obtain weather and traffic information and to plan their routes by searching for hotels, attractions, travel guides, and other tourists’ opinions.
Traditional methods are based on statistical, econometric, and artificial intelligence tools (Song, Witt, & Li, 2008) and historical data (Pan, Wu, & Song, 2012).
The availability of real-time, high-volume, and high frequency data has revolutionized the way in which tourist behavior is monitored and forecasting reliability is achieved (Yang, Pan, & Song, 2014). Information search patterns have been proven to be indicators of future behavior (Park, Lee, & Song, 2017). Data from search engines are widely applied in the tourism domain to forecast destination-level indicators, such as hotel room demand (e.g. Pan et al., 2012; Yang et al., 2014) and international tourist and visitor arrivals (e.g. Li, Pan, Law, & Huang, 2017; ?nder, 2017; Park et al., 2017). Individual attractions traditionally receive less attention from academicians. Although a trend of shortening time lag has been observed between the travel planning stage and the actual trip, decisions regarding a destination and a hotel are frequently made during pre-trip phase.
In comparison to them, the complexity of attraction visits forecast increases because the choice for a particular point of interest can be made immediately before and during a trip (Xiang et al., 2015). Tourist decision-making is influenced by a wide range of internal and external factors; hence, the importance of “nowcasting” as a capability to quickly and accurately identify current changes in consumer behavior (Choi & Varian, 2012) increases. However, the capability of information search patterns data in predicting attraction demand remains underexplored (Huang, Zhang, & Ding, 2017). The current study aims to investigate the opportunity to predict the number of visits based on tourist online search and social behavior.
We study the problem of predicting likely places of visit of users using their past tweets. What people write on their microblogs reflects their intent and desire relating to most of their common day interests. Taking this as a strong evidence, we hypothesize that tweets of the person can also be treated as source of strong indicator signals for predicting their places of visits. In this paper, we propose a novel approach for predicting place of visit within a given geospatial range considering the past tweets and the time of visit.
The Data collected from google trends, twitter and electronic database CBS StatLine (2018) will be used to predict tourists will be arrived in Amsterdam. the researcher needs to use search queries (keywords) related to tourism in Amsterdam and twitter messages related keywords will be extracted using API, to apply this technique we proposed Hidden Markov model as solution to predict future tourists in Amsterdam.
Problem statement
Since the touristic demand pressures Amsterdam, the city designed a dispersion policy and introduces tourists to other attractive sights and places (ING Economisch Bureau, 2016).
With this policy, the city provides other places with the opportunity to gain from tourism and stimulates an even bigger contribution of the tourism industry to the Dutch economy (ING Economisch Bureau, 2016).
Tourism demand modelling and predicting has attracted much attention of both academics and practitioners (H?pken, Ernesti, Fuchs, Kronenberg, & Lexhagen, 2017). Advances in information technologies have given rise to a massive amount of big data, generated by users.
This data includes search query data, social media mentions, and mobile device locations (Mayer-Sch?nberger & Cukier, 2013). Among the previous years, different variables were used to measure tourism demand, tourist arrival, holiday tourist arrival and business tourist arrival were the most popular measures (Song, Li, Witt, & Fei, 2010) the difference being the nature of the arrivals. Also, tourist expenditure in the destination was often used as the demand variable (Kulendran & Wong, Modeling Seasonality in Tourism Forecasting, 2005).[2]
As discussed above, big data may provide new possibilities for forecasting tourism demand. However, there are some challenges regarding the analysis, capture, search, sharing, storage, transfer, and visualization and information privacy of big data.
These challenges require new programs or technologies to uncover hidden values from these large amounts of data (Hashem, et al., 2015). Google Trends and Twitter might be one of those programs needed to enlarge the advantage of the use of big data for predicting tourism demand.
Research Objectives
From a theoretical perspective, this study contributes to literature concerning forecasting tourism demand and extends on Google Trends literature. This thesis analyses the forecasting potential of Google Trends and twitter for tourism demand as overnight stays in hotels, which enhances the current knowledge about tourism demand, Twitter and Google Trends.
The practical relevance of this study lies in the possibility of using Google Trends and twitter for predicting tourism demand in the form of overnights stays in hotels. The use of a free method for predicting demand based on online actions of actual tourists, provides the hospitality and tourism industry with the opportunity to respond more accurately to the demand, which might lead to better experiences for the tourists and better achievements for the companies. The practical relevance for the city of Amsterdam is the possibility to react more accurately to the demand and maintain their dispersion policy. This might lead to a better spread of tourism in the Netherlands and a larger contribution to the Dutch economy. The total hospitality and tourism industry can benefit from this research since it might provide them with new opportunities for intervening in the customer journey.
Research Questions
This has led to the following research question for this study:
- (RQ)1. what extent is the data provided by Google Trends and twitter useful for forecasting night passes in hotels in Amsterdam?
To be able to answer this central research question the following sub-questions will be answered first: –
- (RQ)2. What predicting techniques are used for predicting tourism demand now?
- (RQ)3. What is Google Trends and how does it work?
- (RQ)4. What is twitter and how does it work?
- (RQ)5. Is Google Trends and twitter better predictor than the traditional forecasting method for tourism in Amsterdam?
Thesis outlines
There are several chapters written for this thesis. The first chapter, written above is Introduction, handles the problem statement, research objectives relevance, research questions. The following chapter gives Literature review for forecasting tourism demand, Google Trends, Twitter and the customer journey. The third chapter describes the Methodology for this study and the fourth chapter presents the Results of the data collection and analysis. The final chapter presents the Conclusions of this research, the discussion, the limitations and the indications for further research.
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