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Machine learning 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. 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. Prototypical Implementation: 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. Tourists can use the tool to evaluate easily and fast the probability of fake reviews for a given hotel. Business users (like hotel owners) can use HOTFRED to acquire fake review detection capabilities or to develop existing ones; this stands in-line with the current research discussion focusing on fake reviews (e.g., [19]). No research is without limitations. First, not all possible detection algorithms could have been implemented and evaluated so far. This aspect is going-to be addressed in future research. Furthermore, it is technically not feasible to have a system with 100% correctness. Aiming to reduce the failure rate, the flexible fake detection system uses different analytical components at the same time. The system is implemented by a prototype. In the future it has to be tested more and confronted with new and original classified fake data to ensure a good evaluation and accuracy. In general, it is hard to get actual fake data to evaluate several systems. Due to the on-going research project, it is planned to further evaluate and extend our detection system by the following steps. At first, more analytical components will be integrated (e.g., hotel environment checker, reviewer behavior checker as recommend also in the literature (e.g. [2])) as well as several software tests will be run. Also, more complex models and combinations of it (e.g. through neural networks) are well-suited opportunities for future improvements and adaptions. Additionally, a qualitative evaluation of the system with experts from the hospitality sector as well as different tourist groups are intended. Furthermore, it is considered to extend the tool to collect review data from different review platforms like Yelp and Google Places and show a combined analysis and results view. After extensions, it is planned to make the tool public for research and practice as well and collect further feedback to expand promising future research directions. replica bags1 1 replica bags
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Machine learning 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. 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. Prototypical Implementation: 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. Tourists can use the tool to evaluate easily and fast the probability of fake reviews for a given hotel. Business users (like hotel owners) can use HOTFRED to acquire fake review detection capabilities or to develop existing ones; this stands in-line with the current research discussion focusing on fake reviews (e.g., [19]). No research is without limitations. First, not all possible detection algorithms could have been implemented and evaluated so far. This aspect is going-to be addressed in future research. Furthermore, it is technically not feasible to have a system with 100% correctness. Aiming to reduce the failure rate, the flexible fake detection system uses different analytical components at the same time. The system is implemented by a prototype. In the future it has to be tested more and confronted with new and original classified fake data to ensure a good evaluation and accuracy. In general, it is hard to get actual fake data to evaluate several systems. Due to the on-going research project, it is planned to further evaluate and extend our detection system by the following steps. At first, more analytical components will be integrated (e.g., hotel environment checker, reviewer behavior checker as recommend also in the literature (e.g. [2])) as well as several software tests will be run. Also, more complex models and combinations of it (e.g. through neural networks) are well-suited opportunities for future improvements and adaptions. Additionally, a qualitative evaluation of the system with experts from the hospitality sector as well as different tourist groups are intended. Furthermore, it is considered to extend the tool to collect review data from different review platforms like Yelp and Google Places and show a combined analysis and results view. After extensions, it is planned to make the tool public for research and practice as well and collect further feedback to expand promising future research directions. replica bags1 1 replica bags
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It was one of the first states to record more $200 million in monthly betting handle and in 2022 joined the top 6 states in dollars bet all-time.
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Alabama may eventually opt to go the same route as neighboring Tennessee, which runs an exclusive online market.
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knox and grainger rutherford county humphreys county roane & indian lands knox county indian lands anderson & claiborne rutherford, smith & warren indian lands washington county davidson, dickson, montgomery, & robertson henderson, mcnairy & madison grainger & hawkins jackson & overton jefferson county bedford, warren & franklin haywood, madison, dyer & gibson white, bledsoe, rhea, morgan, fentress & putnam part of north carolina perry county franklin, cannon, jackson & white montgomery and robertson indian lands indian lands morgan, overton & white rutherford & indian lands indian lands indian lands hawkins & knox counties washington county coffee, warren & franklin jefferson, grainger & greene rhea & indian lands hawkins & claiborne hardin & indian lands indian lands sullivan county indian lands indian lands indian lands dickson county dickson, humphreys, montgomery & stewart stewart county smith county & indian lands greene & hawkins carter county greene & hawkins obion county haywood, dyer & tipton hickman & indian lands hickman, lawrence, maury & wayne bedford county roane, monroe, blount & mcminn smith and sumner counties indian lands indian lands giles, bedford, lincoln & maury williamson & indian lands indian lands hardin county rhea county indian lands tennessee county bedford, lincoln & franklin anderson & roane indian lands jackson & indian lands humphreys & hickman fentress & overton mcminn & bradley fentress, jackson, smith, white & overton roane county knox & indian lands tennessee & sumner davidson, williamson & wilson anderson, campbell, fentress & morgan counties hamilton, marion & warren jefferson county chickasaw nation lands sumner & indian lands montgomery county washington county davidson county shelby county wilson, macon, smith & sumner counties washington & carter grainger, claiborne, campbell, anderson & knox warren & white white, jackson, smith & indian lands part of north carolina hickman county indian lands jackson & smith davidson county sumner county |
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