Related references
Note: Only part of the references are listed.Creating and detecting fake reviews of online products
Joni Salminen et al.
JOURNAL OF RETAILING AND CONSUMER SERVICES (2022)
An Adaptive Social Spammer Detection Model With Semi-Supervised Broad Learning
Tie Qiu et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2022)
Dynamic Neural Networks: A Survey
Yizeng Han et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)
Resampling imbalanced data to detect fake reviews using machine learning classifiers and textual-based features
Gregorius Satia Budhi et al.
MULTIMEDIA TOOLS AND APPLICATIONS (2021)
Spam review detection using self attention based CNN and bi-directional LSTM
P. Bhuvaneshwari et al.
MULTIMEDIA TOOLS AND APPLICATIONS (2021)
Deep Graph neural network-based spammer detection under the perspective of heterogeneous cyberspace
Zhiwei Guo et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2021)
Fake reviews classification using deep learning ensemble of shallow convolutions
Muhammad Saad Javed et al.
JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE (2021)
Fake Reviews Detection: A Survey
Rami Mohawesh et al.
IEEE ACCESS (2021)
A burst-based unsupervised method for detecting review spammer groups
Shu-Juan Ji et al.
INFORMATION SCIENCES (2020)
Learn#: A Novel incremental learning method for text classification
Guangxu Shan et al.
EXPERT SYSTEMS WITH APPLICATIONS (2020)
Generating behavior features for cold-start spam review detection with adversarial learning
Xiaoya Tang et al.
INFORMATION SCIENCES (2020)
PV-DAE: A hybrid model for deceptive opinion spam based on neural network architectures
Anass Fahfouh et al.
EXPERT SYSTEMS WITH APPLICATIONS (2020)
Spam Review Detection Using the Linguistic and Spammer Behavioral Methods
Naveed Hussain et al.
IEEE ACCESS (2020)
Dynamic Feature Selection for Spam Detection in Twitter
M. Salih Karakasli et al.
INTERNATIONAL TELECOMMUNICATIONS CONFERENCE, ITELCON 2017 (2019)
Enactment of Ensemble Learning for Review Spam Detection on Selected Features
Faisal Khurshid et al.
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS (2019)
State-of-art approaches for review spammer detection: a survey
Rupesh Kumar Dewang et al.
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS (2018)
Graph-based review spammer group detection
Zhuo Wang et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2018)
A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach
Renzhi Lu et al.
APPLIED ENERGY (2018)
Hybrid approach of improved binary particle swarm optimization and shuffled frog leaping for feature selection
S. P. Rajamohana et al.
COMPUTERS & ELECTRICAL ENGINEERING (2018)
DRI-RCNN: An approach to deceptive review identification using recurrent convolutional neural network
Wen Zhang et al.
INFORMATION PROCESSING & MANAGEMENT (2018)
Towards automatic filtering of fake reviews
Emerson F. Cardoso et al.
NEUROCOMPUTING (2018)
Customer engagement and online reviews
Rakhi Thakur
JOURNAL OF RETAILING AND CONSUMER SERVICES (2018)
Online Reviewer Engagement: A Typology Based on Reviewer Motivations
Charla Mathwick et al.
JOURNAL OF SERVICE RESEARCH (2017)
What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews
Dongsong Zhang et al.
JOURNAL OF MANAGEMENT INFORMATION SYSTEMS (2016)
Detection of review spam: A survey
Atefeh Heydari et al.
EXPERT SYSTEMS WITH APPLICATIONS (2015)
Measuring classifier performance: a coherent alternative to the area under the ROC curve
David J. Hand
MACHINE LEARNING (2009)
Online consumer review: Word-of-mouth as a news element of marketing communication mix
Yubo Chen et al.
MANAGEMENT SCIENCE (2008)
Artificial neural networks: fundamentals, computing, design, and application
IA Basheer et al.
JOURNAL OF MICROBIOLOGICAL METHODS (2000)