Neural networks for deceptive opinion spam detection: An empirical study

Ren, Yafeng; Ji, Donghong

doi:10.1016/j.ins.2017.01.015

Cited by 196 publications

(121 citation statements)

References 14 publications

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“…: updated spam list (1) Let is the th email of , is the th item of , is th contact's trust, is th contact's similarity, is the trust of the sender of , is the number of emails, is the number of items in , is the number of contacts, is the generated spam report, ℎ : trust threshold, ℎ : interest similarity threshold, flag is the subject of email (2) for ← 1 to do (4) if flag = "spam report" if ≥ ℎ +1 ← content of the email ← + 1 for ← 1 to do (8) if ≥ ℎ forwarding to contact end if (10) end for end if else…”

Section: Inputmentioning

confidence: 99%

“…for ← 1 to do (8) if ≥ ℎ sending to contact end if (10) end for (11)end for (13)return according to their relationships [9][10][11]. Social trust is a key factor that affects the sharing of knowledge and the development of social relationships [12,13]: users are more likely to accept suggestions from others with high trust value and interests similarity [14].…”

Section: Inputmentioning

confidence: 99%

“…Most spam filters were developed to identify complete-spam [6,7]. The accuracy of spam detection of some of these filters can be fairly high [8]. However, very often we still find spam in our email inbox.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing

Liu

Zou

Zhang

et al. 2017

Wireless Communications and Mobile Computing

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Email spam consumes a lot of network resources and threatens many systems because of its unwanted or malicious content. Most existing spam filters only target complete-spam but ignore semispam. This paper proposes a novel and comprehensive CPSFS scheme: Credible Personalized Spam Filtering Scheme, which classifies spam into two categories: complete-spam and semispam, and targets filtering both kinds of spam. Complete-spam is always spam for all users; semispam is an email identified as spam by some users and as regular email by other users. Most existing spam filters target complete-spam but ignore semispam. In CPSFS, Bayesian filtering is deployed at email servers to identify complete-spam, while semispam is identified at client side by crowdsourcing. An email user client can distinguish junk from legitimate emails according to spam reports from credible contacts with the similar interests. Social trust and interest similarity between users and their contacts are calculated so that spam reports are more accurately targeted to similar users. The experimental results show that the proposed CPSFS can improve the accuracy rate of distinguishing spam from legitimate emails compared with that of Bayesian filter alone.

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Section: Inputmentioning

confidence: 99%

Section: Inputmentioning

confidence: 99%

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CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing

Liu

Zou

Zhang

et al. 2017

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…In order to make more profits, some merchants hire writers to write positive reviews to promote their products or write negative reviews to damage the business of their competitors [1]. With the spread and growth of deceptive reviews, more and more research [2][3][4][5][6][7][8][9] is focusing on the detection of deceptive comments.…”

Section: Introductionmentioning

confidence: 99%

“…Early representative works [2][3][4][5] generally extract features manually and use machine learning algorithms to solve the problem. As the neural networks model is widely used in natural language processing, more and more research [6,7] builds an end-to-end neural network model to extract the document representation from the review automatically which obtains the better classification results.…”

Section: Introductionmentioning

confidence: 99%

A Review Structure Based Ensemble Model for Deceptive Review Spam

Zeng

Lin

Chen³

et al. 2019

Information

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Consumers’ purchase behavior increasingly relies on online reviews. Accordingly, there are more and more deceptive reviews which are harmful to customers. Existing methods to detect spam reviews mainly take the problem as a general text classification task, but they ignore the important features of spam reviews. In this paper, we propose a novel model, which splits a review into three parts: first sentence, middle context, and last sentence, based on the discovery that the first and last sentence express stronger emotion than the middle context. Then, the model uses four independent bidirectional long-short term memory (LSTM) models to encode the beginning, middle, end of a review and the whole review into four document representations. After that, the four representations are integrated into one document representation by a self-attention mechanism layer and an attention mechanism layer. Based on three domain datasets, the results of in-domain and mix-domain experiments show that our proposed method performs better than the compared methods.

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Review Spam Detection Using Word Embeddings and Deep Neural Networks

Barushka

Hájek

2019

IFIP Advances in Information and Communication Technology

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Review spam (fake review) detection is increasingly important taking into consideration the rapid growth of internet purchases. Therefore, sophisticated spam filters must be designed to tackle the problem. Traditional machine learning algorithms use review content and other features to detect review spam. However, as demonstrated in related studies, the linguistic context of words may be of particular importance for text categorization. In order to enhance the performance of review spam detection, we propose a novel content-based approach that considers both bag-of-words and word context. More precisely, our approach utilizes ngrams and the skip-gram word embedding method to build a vector model. As a result, high-dimensional feature representation is generated. To handle the representation and classify the review spam accurately, a deep feed-forward neural network is used in the second step. To verify our approach, we use two hotel review datasets, including positive and negative reviews. We show that the proposed detection system outperforms other popular algorithms for review spam detection in terms of accuracy and area under ROC. Importantly, the system provides balanced performance on both classes, legitimate and spam, irrespective of review polarity.

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Neural networks for deceptive opinion spam detection: An empirical study

Cited by 196 publications

References 14 publications

CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing

CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing

A Review Structure Based Ensemble Model for Deceptive Review Spam

Review Spam Detection Using Word Embeddings and Deep Neural Networks

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