TY - GEN
T1 - A Fused Siamese Network for Fake Review Detection
AU - Dasgupta, Sankarshan
AU - Buckley, James
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - With the increasing shift to web-based platforms, particularly in e-commerce, physical verification of products has become limited. As a result, virtual verification through customer reviews plays a crucial role in influencing purchasing decisions. However, the presence of fake reviews compromises the trustworthiness of these decisions. This paper introduces a novel approach for detecting fake reviews by fusing embeddings to generate high-dimensional input for the identical arms of a Siamese network. Specifically, MiniLM BERT embeddings capture contextual relationships, while Word2Vec embeddings focus on semantic relationships. This fusion of embeddings into a unified high dimensional vector enhances the training of the Siamese network with LSTM layers, improving its ability to learn meaningful patterns. The output is further processed through a fuzzy logic system to ensure a robust decision-making process. Experimental results demonstrate significant improvements over state-of-the-art methods in fake review detection.
AB - With the increasing shift to web-based platforms, particularly in e-commerce, physical verification of products has become limited. As a result, virtual verification through customer reviews plays a crucial role in influencing purchasing decisions. However, the presence of fake reviews compromises the trustworthiness of these decisions. This paper introduces a novel approach for detecting fake reviews by fusing embeddings to generate high-dimensional input for the identical arms of a Siamese network. Specifically, MiniLM BERT embeddings capture contextual relationships, while Word2Vec embeddings focus on semantic relationships. This fusion of embeddings into a unified high dimensional vector enhances the training of the Siamese network with LSTM layers, improving its ability to learn meaningful patterns. The output is further processed through a fuzzy logic system to ensure a robust decision-making process. Experimental results demonstrate significant improvements over state-of-the-art methods in fake review detection.
KW - Embedding Fusion
KW - Fuzzy Logic
KW - Siamese Network
UR - https://www.scopus.com/pages/publications/105014413382
U2 - 10.1007/978-3-031-94953-1_21
DO - 10.1007/978-3-031-94953-1_21
M3 - Conference contribution
AN - SCOPUS:105014413382
SN - 9783031949524
T3 - Communications in Computer and Information Science
SP - 257
EP - 270
BT - Computational Science and Computational Intelligence - 11th International Conference, CSCI 2024, Proceedings
A2 - Arabnia, Hamid R.
A2 - Deligiannidis, Leonidas
A2 - Shenavarmasouleh, Farzan
A2 - Amirian, Soheyla
A2 - Ghareh Mohammadi, Farid
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Computational Science and Computational Intelligence, CSCI 2024
Y2 - 11 December 2024 through 13 December 2024
ER -