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Designing a deep learning-based application for detecting fake online reviews

Abstract

In the era of prevalent online commerce, online reviews significantly influence purchasing decisions. Unfortunately, this has also led to the emergence of fake reviews, which can deceive consumers and undermine trust in online platforms. Our study addresses this issue by developing DenyBERT, a deep learning-based software that enhances the Bidirectional Encoder Representations from Transformers (BERT) framework with Deep and Light Transformation (DeLighT) and Knowledge Distillation (KD) techniques. These innovations not only reduce computational demands but also improve the model 's accuracy in identifying fake reviews, making it ideally suited for real-world applications. Significantly, DenyBERT requires only 16.01M parameters-significantly fewer than its predecessors such as BERT and TinyBERT-yet achieves a robust accuracy of 96.12% and an F1-score of 96.47%. This efficiency makes it particularly suited for deployment on devices with limited processing capabilities. The software, developed in Python, features a flexible input mechanism that allows for analyzing reviews directly from websites via URL or via manual input of review paragraphs. Our findings indicate that DenyBERT outperforms existing models in both speed and accuracy, making it a powerful tool for combating fake reviews in real -time scenarios. This advancement not only enhances user trust in online review systems but also supports ecommerce platforms in maintaining a fair and transparent market environment.

article Review

date_range 2024

language English

link Link of the paper

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Thao Le

Kien Do

Thuy Dang

Thuy Luong

Bach Ngo

Duc Tran

Bach Hoang

Cuong Duong

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Featured Keywords

Fake online review

Natural language processing

Bidirectional encoder representations from transformers

Deep and light transformation

Knowledge distillation

Single head-attention

shopping_basket PURCHASE DATA

Assessment of reinforced concrete building damages following the Kahramanmaraş earthquakes in Malatya, Turkey (February 6, 2023)

Abstract

Earthquakes of magnitude Mw = 7.7 and Mw = 7.6 hit the city of Kahramanmaraş (Turkey) on 6 February 2023. These earthquakes caused serious damage in 11 provinces in Turkey resulting in over 50,000 deaths and huge economic losses with the collapse of thousands of buildings. Two consecutive devastating earthquakes have severely damaged the reinforced concrete building stock located in Malatya. There are many reasons reinforced concrete structures suffer damage under the influence of seismic loads. These reasons can be listed as material and section properties that do not comply with earthquake codes, workmanship defects, ground parameters that are not considered and design errors, as seen in field investigations after earthquakes in Turkey in the past. In this research, a field study was conducted on reinforced concrete buildings damaged after the earthquakes in Malatya province. The causes of structural damages in reinforced concrete buildings between 6–26 years of age were assessed in terms of design and material defects. The structural damages were mainly caused due to insufficient stirrups in structural members, short column, strong beam–weak column, failures of infill walls, soft story type collapse, poor concrete quality, and reinforcement corrosion. In addition to the field investigation, the spectrum relationships created using raw acceleration data obtained from DEMP (Disaster and Emergency Management Presidency) stations in Malatya province were compared with the design response spectra.

article Article

date_range 2024

language English

link Link of the paper

Collapsed buildings

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