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The importance to cope with online fake reviews in Tourism becomes more and more evident. In the hotel sector hoteliers as well as guests often struggle with the challenges to separate true and fake reviews from each other. Therefore, our research introduces HOTFRED - a flexible hotel fake review detection system - as part of an on-going research project. By combining different analytical approaches, the HOTFRED system indicates via an aggregated probability whether a review is true or fake. As the evaluation of the prototypical implementation showed, this approach can support to detect fake reviews. Many different stakeholders in the Tourism sector can profit from this automatic tool. Thus, hoteliers can take measures to safe their reputation, guests can benefit in their decision-making process and research might use the tool as an initial starting point for future research in the area of fake information. Online reviews Fake reviews A design science research approach was chosen [7, 12] to create a hotel fake review detection system with the aim to answer the proposed research question. Regarding the recommendations, the problem of fake review detection and need of a software solution were identified in Sect. 1 and 2 of the paper. Previous published research work (e.g., [2, 6, 8, 9, 11]) as well as setup several design workshops with several participants were reviewed to collect the relevant objectives of the solution. The primary objective of the system is to determine the probability of fake reviews for a given hotel using several analytical approaches. The system should gather data of the individual reviews collected from online review sites as well as information about the reviewers and the hotel itself. It should have the capability to integrate more analytical approaches stepwise over the time to improve accuracy and integrate current research results. Furthermore, components should be selectable and de-selectable case by case. Based on these objectives, a hotel fake review detection system based on different components has been created. At first, online hotel reviews and related meta data (such as hotel name, reviewers, etc. [2]) have to be collected through a web crawling tool [21] from online review sides like TripAdvisor. These data should be stored in a central database. Thus, central and fast accessible place for data access for the analytical components is shared. In the following, the analytical components (here: (1) text mining-based classification and (2) spell checker) can fall back on the needed data to calculate the probability of fake reviews for a given hotel in a related time frame. The text mining-based classification (1) will use already classified hotel fake review data to calculate the probability of a fake by evaluating textual similarities. The spell checker (2) will calculate a probability based on the amount spelling and grammar issues. Furthermore, the reviewer behavior checker uses data (e.g. timings, hotels, etc. [2]) about the last written reviews of the reviewer to infer on fakes. The hotel environment checker uses data about the hotel to identify fake or incorrect information (e.g., location, stars, facilities). After all components (1 and 2) are analyzed, a scoring system [17] uses the individual probabilities to determine the final probability of fake reviews for a given hotel. The scoring system can run a weighted or unweighted average of the different probabilities. The weights can be adjusted based on trained models and validation after system use. The system architecture is summarized in the following Fig. 1 and allows analytical extensions in the future. Dotted components (reviewer behavior checker, hotel environment checker) are not implemented currently, but will follow up as a part of future research. For a first demonstration a prototype was implemented as explained in the following section. Full size image 4 First Prototype Development and First Evaluation Prototypical Implementation: 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. faux saint laurent bag
June 18, 2023 – The 29 Palms Band of Mission Indians has introduced a social betting platform. DraftKings CEO Jason Robins made it clear during a conference that legalizing sports betting in California is still a long way off. – DraftKings highlights California sports betting prop 27 In an email to users. Six Reasons to Bet on These California Betting Sites : Paiute Palace Casino Kings County : Tachi Palace Hotel & Casino The Kings are the oldest team in the National Basketball Association. They are still based in SF, but now represent the entire state. to win the 2023-2024 NFL Coach of the Year award. faux saint laurent bagfaux saint laurent bag
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The importance to cope with online fake reviews in Tourism becomes more and more evident. In the hotel sector hoteliers as well as guests often struggle with the challenges to separate true and fake reviews from each other. Therefore, our research introduces HOTFRED - a flexible hotel fake review detection system - as part of an on-going research project. By combining different analytical approaches, the HOTFRED system indicates via an aggregated probability whether a review is true or fake. As the evaluation of the prototypical implementation showed, this approach can support to detect fake reviews. Many different stakeholders in the Tourism sector can profit from this automatic tool. Thus, hoteliers can take measures to safe their reputation, guests can benefit in their decision-making process and research might use the tool as an initial starting point for future research in the area of fake information. Online reviews Fake reviews A design science research approach was chosen [7, 12] to create a hotel fake review detection system with the aim to answer the proposed research question. Regarding the recommendations, the problem of fake review detection and need of a software solution were identified in Sect. 1 and 2 of the paper. Previous published research work (e.g., [2, 6, 8, 9, 11]) as well as setup several design workshops with several participants were reviewed to collect the relevant objectives of the solution. The primary objective of the system is to determine the probability of fake reviews for a given hotel using several analytical approaches. The system should gather data of the individual reviews collected from online review sites as well as information about the reviewers and the hotel itself. It should have the capability to integrate more analytical approaches stepwise over the time to improve accuracy and integrate current research results. Furthermore, components should be selectable and de-selectable case by case. Based on these objectives, a hotel fake review detection system based on different components has been created. At first, online hotel reviews and related meta data (such as hotel name, reviewers, etc. [2]) have to be collected through a web crawling tool [21] from online review sides like TripAdvisor. These data should be stored in a central database. Thus, central and fast accessible place for data access for the analytical components is shared. In the following, the analytical components (here: (1) text mining-based classification and (2) spell checker) can fall back on the needed data to calculate the probability of fake reviews for a given hotel in a related time frame. The text mining-based classification (1) will use already classified hotel fake review data to calculate the probability of a fake by evaluating textual similarities. The spell checker (2) will calculate a probability based on the amount spelling and grammar issues. Furthermore, the reviewer behavior checker uses data (e.g. timings, hotels, etc. [2]) about the last written reviews of the reviewer to infer on fakes. The hotel environment checker uses data about the hotel to identify fake or incorrect information (e.g., location, stars, facilities). After all components (1 and 2) are analyzed, a scoring system [17] uses the individual probabilities to determine the final probability of fake reviews for a given hotel. The scoring system can run a weighted or unweighted average of the different probabilities. The weights can be adjusted based on trained models and validation after system use. The system architecture is summarized in the following Fig. 1 and allows analytical extensions in the future. Dotted components (reviewer behavior checker, hotel environment checker) are not implemented currently, but will follow up as a part of future research. For a first demonstration a prototype was implemented as explained in the following section. Full size image 4 First Prototype Development and First Evaluation Prototypical Implementation: 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. faux saint laurent bag
June 18, 2023 – The 29 Palms Band of Mission Indians has introduced a social betting platform. DraftKings CEO Jason Robins made it clear during a conference that legalizing sports betting in California is still a long way off. – DraftKings highlights California sports betting prop 27 In an email to users. Six Reasons to Bet on These California Betting Sites : Paiute Palace Casino Kings County : Tachi Palace Hotel & Casino The Kings are the oldest team in the National Basketball Association. They are still based in SF, but now represent the entire state. to win the 2023-2024 NFL Coach of the Year award. faux saint laurent bagfaux saint laurent bag
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faux saint laurent bag faux saint laurent bag faux saint laurent bag faux saint laurent bag faux saint laurent bag faux saint laurent bag faux saint laurent bag faux saint laurent bag faux saint laurent bag faux saint laurent bag faux saint laurent bag faux saint laurent bag faux saint laurent bagThe betting site will offer a bet at the end of the day that is based on the bet you made on the day you bet on the day you made the bet. It also offers a betting service that you can use for any kind of sports betting. This service is available for all sports betting, but you can only bet on sports betting with a betting service that you can use for a specific kind of sports betting. The betting service will also offer betting services that you can use for sports betting.What is Betting? Betting is the most popular sports betting site on the Internet. This service is available for all sports betting, but you can only bet on sports betting with a betting service that you can use for a specific kind of sports betting. Betting Services can be used for any kind of sports betting. What is Betting Services? faux saint laurent bag |
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