Content analysis of fake consumer reviews by survey-based text categorization

Moon, Sangkil; Kim, Moon-Yong; Iacobucci, Dawn

doi:10.1016/j.ijresmar.2020.08.001

Cited by 61 publications

(52 citation statements)

References 73 publications

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“…Proposed conceptual and process models can serve as inspiration for the introduction of systematic and thus more credible procedures for reviewing tourism services, attractions and destinations. This approach is very important for e-marketing, especially at the present time, when increased attention is being paid in expert assessments, scientific articles and media to fake reviews (Streitfield 2011;Smith 2013;Lipson 2016;Zhang et al 2016;Song et al 2017;Buccafurri et al 2018;Cardoso et al 2018;Barbado et al 2019;Fedeli 2020;Wu et al 2020;Kauffmann et al 2020;Gadek and Guélorget 2020;Moon et al 2020). The presented models should be verified by simulation based on real data, which demonstrates a reduction in the share of published false reviews and thus an increase in trust in review sites.…”

Section: Discussionmentioning

confidence: 94%

“…• statistical methods (Hu et al 2012;Song et al 2017;Harris 2018;Kumar et al 2019), • text analysis (Harris 2018;Barbado et al 2019;Kauffmann et al 2020), which in recent years includes sentiment/emotional analysis and its relationship to overall evaluation (Harris 2018;Martinez-Torres and Toral 2019;Valdivia et al 2019;Gadek and Guélorget 2020;Moon et al 2020),…”

Section: Methods For Detecting False Reviewsmentioning

confidence: 99%

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Trust Model for Online Reviews of Tourism Services and Evaluation of Destinations

Zelenka

Azubuike

Pašková

2021

Administrative Sciences

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Obtaining information about destinations and services they provide is ever more based on user-generated content (UGC), which includes reviews of tourism services as well as evaluation of attractions and destinations by visitors. The growing importance of reviews of tourism services is recognized by tourism service providers, and some of them influence the content of reviews on review sites. At the same time, procedures for the prevention of false and misleading reviews, as well as their detection, are being intensively developed. This is documented in relevant sources, which were identified especially on the Web of Science, Scopus, Sciencedirect, Researchgate and the websites of MDPI, Emerald and Taylor & Francis Online. The aim of this study is to reveal how the verification of reviews can be improved with the intention to increase confidence in the review sites. In the form of case studies of TripAdvisor and Booking.com, the current rise of trust in reviews on these review sites was analysed and documented. The outputs of research include a SWOT analysis, processual analysis and an analysis of verification process, conditions, factors affecting trust in reviews on review sites. On these bases, a conceptual model for providing verified reviews of tourism services or verified destination assessment and two process models for providing verified reviews of tourism services and for providing verified destination assessment have been drawn up.

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Section: Discussionmentioning

confidence: 94%

Section: Methods For Detecting False Reviewsmentioning

confidence: 99%

Trust Model for Online Reviews of Tourism Services and Evaluation of Destinations

Zelenka

Azubuike

Pašková

2021

Administrative Sciences

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“…There is a need for higher institutions to offer various English translation practices and introduce English translation methods into classroom teaching. English translation is the main manifestation of students' comprehensive English proficiency and evaluation of their English practical abilities [21]. Most importantly, more opportunities should be provided for students to practice translation outside the classroom, cultivate their interest in learning, and improve their learning enthusiasm.…”

Section: Basic Concept and Technical Routementioning

confidence: 99%

Design of Shared Internet of Things System for English Translation Teaching Using Deep Learning Text Classification

Guo

Lin

2022

Wireless Communications and Mobile Computing

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The purpose is to adapt to the current social development and promote the English translation teaching reform. Based on the theories of deep learning (DL), text classification (TC), and the Internet of Things (IoT), this work analyzes the current situation of English translation teaching. Additionally, 100 text categories are selected from the English text corpus of Northwestern Polytechnic University as the research objects. The data are classified by the DL-based TC method and analyzed by introducing the simulated annealing algorithm. Finally, the storage and security performance of the shared IoT system are described. The results show that the proposed TC method can overcome the performance loss caused by the function extraction method, greatly reducing the training time and function space. The storage and security performance of the shared IoT system to encrypt English text will increase with the number of model iterations. Therefore, this work designs the English translation teaching-oriented shared IoT system using a DL-based TC. The finding plays an important role in subsequent English translation and enriching the theory of IoT.

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“…The trust-building capacity of star ratings as well as the financial premium of a good reputation has been demonstrated repeatedly and for various contexts (Abramova et al 2017;Teubner et al 2017). Consequently, recent research has started to shift on the pitfalls and issues of rating systems, such as rating skewness (Teubner and Glaser 2018), rating inflation over time (Filippas et al 2017), fake reviews (Moon et al 2020;Wu et al 2020), as well as rating response mechanisms and strategies (Abramova et al 2015;Greiner et al 2020).…”

Section: Rating Scoresmentioning

confidence: 99%

On the Potency of Online User Representation: Insights from the Sharing Economy

2021

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Online user representation (UR) is a cornerstone of platform-mediated interactions within the sharing economy. While the general usefulness of UR artifacts for facilitating online and offline interactions is widely acknowledged and understood, the underlying mechanisms and operating principles often require a more detailed analysis. In this chapter, we thus introduce a systematic framework grounded in signaling and social presence theory for analyzing UR artifacts for online platforms in general—and the sharing economy in particular. We apply our framework as a structural lens in a case study on user profiles on Airbnb, unveiling structural similarities and differences between the opposing market sides. We discuss our findings against the backdrop of emerging information systems research directions and suggest paths for future work on the sharing economy.

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Content analysis of fake consumer reviews by survey-based text categorization

Cited by 61 publications

References 73 publications

Trust Model for Online Reviews of Tourism Services and Evaluation of Destinations

Trust Model for Online Reviews of Tourism Services and Evaluation of Destinations

Design of Shared Internet of Things System for English Translation Teaching Using Deep Learning Text Classification

On the Potency of Online User Representation: Insights from the Sharing Economy

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