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In general, there are two ways tourists can detect hotel fake reviews before booking. They are typically either reviewing it manually based on different heuristics (e.g., [5]) or using software tools (e.g., [1, 6]). Due to sparse research on a combination and flexible implementation of different approaches for automatic fake review classification, the presented research aims to create a new system following the design science research method [7]. Thus, this short paper is part of an ongoing research project addressing the research question "How can a flexible hotel fake review detection system be designed?". In the following a short and comprehensive theoretical background section is given before the proposed solution approach is presented.
First, meta data of the behavior of the reviewer can be used to check the fake probability of reviews [2, 6, 8, 9]. For instance, if the reviewer writes a lot of hotel reviews in a short time period, this might help to identify fake reviews. Besides meta data, a fake detection can be carried out for example by investigating the writing style, grammar, spelling etc. [10]. Special data sources for training of fake classification models are provided by Yelp.de and through research using crowdsourcing approaches to write fake reviews [4, 10]. Furthermore, research focuses on the comparison of different textual styles between fake and non-fake classified texts [2, 4, 11]. The specific use of some phrases or special spelling issues can lead to the detection of fake reviews, too. Further, there are some online platforms (e.g., reviewmeta.com) providing assistance to identify fake reviews from Amazon. However, flexible hotel review-specific approaches applying different approaches with a scientifical base are still missing. Thus, the research wants to address this gap by providing a flexible fake detection system based on different adjusted approaches.
figure 1
A flexible hotel fake detection system - called HOTFRED - was implemented within a first prototype according to general recommendations coming from previous research [13]. The prototype focuses on the main components to collect data on two major analytical components (1) text mining-based classification and (2) spell checker as well as the scoring system to provide the user (here: Tourist) an aggregated, comprehensive information. A web crawler tool [21] was developed in Python to collect the review data from tripadvisor.com. The web crawler has to collect different data of the hotel (e.g., name, URL, class) and the review (e.g., date, review text, points) to run a proper fake review detection and related analysis. After data receiving via HTTPS, it is stored for further analysis within a MySQL database. As a first analytical component (1) a text mining-based fake detection approach was implemented according to the general text pre-processing recommendations [14]. Following, classified fake review data from Yelp was used as a data source for training the classification model [2]. This data set consists of pre-labeled examples regarding the filtered fake characters of hotel reviews written in English. Approximately, 14% of the data can be seen as filtered fake reviews. Existing research already used and validated this data source for e.g. validations [2]. After the evaluation of different classification algorithms (e.g., Support Vector Machines, Naïve Bayes Classifier, KNN), the Support Vector Machine has been chosen as a good fake review classifier based on the accuracy of the classification (e.g., combined metrics like precision, recall, F-score, etc.). For the second analytical component, (2) a spelling checker software tool was developed. This detection component of the system recognizes spelling mistakes based on the ideas of the Levenshtein Distance [15]. The software was programmed in Python. Therefore, the Python library pyspellchecker was used. The scoring system component can use the individual results of the finished analytical components to show a summarized view about the fake probabilities of the reviews for the given hotel.
For the demonstration of the detection system a touristic region in Italy was chosen. Thus, a full sample for all 3 stars and 4 stars hotels in Sorrento/Italy was selected. The sample contained N = 35370 reviews for 79 hotels from 3570 different users. For N = 12 hotels we found a high probability for having fake reviews within the given timeframe (examples are provided by the authors on request). This stands also in line with previous research, that states about 10%–20% of fake reviews on Yelp [16]. Furthermore, already recognized approaches were used and combined via a flexible scoring model. The accuracy of the trained models was ensured by quality metrics recommended in the literature. Furthermore, some workshops with researchers and potential users were performed based on the results to discuss the progress and application of the HOTFRED prototype of the proposed flexible system. For technical software testing and evaluations of system components (1) and (2), a Swagger interface through a FastAPI [18] deployment is currently under development. The system as well as its components (with deployment possibilities as microservices [20]) can be reachable via a user interface as well as a REST-API call. This function is also currently under development. Furthermore, HOTFRED is to be designed as a scalable system with a fast data processing.
The detection of hotel fake reviews is an important topic for research and practice as well. On the one hand, tourists are afraid of taking unfavorable or wrong decisions based on fake reviews. On the other hand, hoteliers are afraid that fake reviews harm their reputation. Therefore, the flexible HOTFRED fake detection system was implemented to cope with the challenges of fake reviews. This approach extends past research (e.g. [2, 4, 9]) in different ways. HOTFRED is designed as a flexible and open tool which enables review detection through different components and allows a case by case selection of these. Therefore, in practice different detection components can be used depending on a use-case specific evaluation. The components can be reached through a defined REST-API, which will be extended and in a currently on-going development project. At the moment, a combined detection approach using a new classified fake detection text model (1) as well as a spell checker (2) is used. In that components, in comparison to other approaches (e.g. [2]), we are using a spell checker focusing on grammar and a classified Yelp dataset not only for validation reasons but also to build a good textual classification model upon it. Additionally, further analytical components as depicted in Fig. 1 are under development. Research as well a practice can benefit from presented research.
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