A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Making large-scale support vector machine learning practical
Advances in kernel methods
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Information retrieval as statistical translation
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Collection statistics for fast duplicate document detection
ACM Transactions on Information Systems (TOIS)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Extraction and representation of contextual information for knowledge discovery in texts
Information Sciences—Informatics and Computer Science: An International Journal
Cluster-based retrieval using language models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Context-sensitive text mining and belief revision for intelligent information retrieval on the web
Web Intelligence and Agent Systems
Detecting phrase-level duplication on the world wide web
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
An evolutionary learning approach for adaptive negotiation agents: Research Articles
International Journal of Intelligent Systems - Learning Approaches for Negotiation Agents and Automated Negotiation
Detecting spam web pages through content analysis
Proceedings of the 15th international conference on World Wide Web
OpinionFinder: a system for subjectivity analysis
HLT-Demo '05 Proceedings of HLT/EMNLP on Interactive Demonstrations
Inferential language models for information retrieval
ACM Transactions on Asian Language Information Processing (TALIP)
Proceedings of the 16th international conference on World Wide Web
Online supervised spam filter evaluation
ACM Transactions on Information Systems (TOIS)
Spam Filtering Using Statistical Data Compression Models
The Journal of Machine Learning Research
Designing novel review ranking systems: predicting the usefulness and impact of reviews
Proceedings of the ninth international conference on Electronic commerce
Spam filtering for short messages
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Detecting splogs via temporal dynamics using self-similarity analysis
ACM Transactions on the Web (TWEB)
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Towards a belief-revision-based adaptive and context-sensitive information retrieval system
ACM Transactions on Information Systems (TOIS)
Partitioned logistic regression for spam filtering
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Trusting spam reporters: A reporter-based reputation system for email filtering
ACM Transactions on Information Systems (TOIS)
Analyzing and Detecting Review Spam
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Exploring linguistic features for web spam detection: a preliminary study
AIRWeb '08 Proceedings of the 4th international workshop on Adversarial information retrieval on the web
A Design Science Research Methodology for Information Systems Research
Journal of Management Information Systems
A Research Agenda for Trust in Online Environments
Journal of Management Information Systems
Stylometric Identification in Electronic Markets: Scalability and Robustness
Journal of Management Information Systems
Modeling and Predicting the Helpfulness of Online Reviews
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
How opinions are received by online communities: a case study on amazon.com helpfulness votes
Proceedings of the 18th international conference on World wide web
Web spam identification through language model analysis
Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web
Toward a Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning
IEEE Transactions on Knowledge and Data Engineering
Link spam target detection using page farms
ACM Transactions on Knowledge Discovery from Data (TKDD)
International Journal of Electronic Commerce
Detecting spammers and content promoters in online video social networks
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Is spam an issue for opinionated blog post search?
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A co-classification framework for detecting web spam and spammers in social media web sites
Proceedings of the 18th ACM conference on Information and knowledge management
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Finding unusual review patterns using unexpected rules
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Toward a semantic granularity model for domain-specific information retrieval
ACM Transactions on Information Systems (TOIS)
Design science in information systems research
MIS Quarterly
Design science and the accumulation of knowledge in the information systems discipline
ACM Transactions on Management Information Systems (TMIS)
Credit Rating Change Modeling Using News and Financial Ratios
ACM Transactions on Management Information Systems (TMIS)
Do Vendors’ Pricing Decisions Fully Reflect Information in Online Reviews?
ACM Transactions on Management Information Systems (TMIS)
NordSec'12 Proceedings of the 17th Nordic conference on Secure IT Systems
A Dispatch-Mediated Communication Model for Emergency Response Systems
ACM Transactions on Management Information Systems (TMIS)
Detecting Deceptive Chat-Based Communication Using Typing Behavior and Message Cues
ACM Transactions on Management Information Systems (TMIS)
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In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.