Opinion spam and analysis

Mining and summarizing customer reviews

Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793

Thumbs up?: sentiment classification using machine learning techniques

Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024

Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews

Peter, P., & Turney (2002) · Cited by 3,646

Thumbs up or thumbs down?

Turney, P. D. (2001) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/1073083.1073153 · Cited by 2,354

Mining the peanut gallery

Dave, K. S., Lawrence, S., & Pennock, D. M. (2003) · International World Wide Web Conference (WWW) · doi:10.1145/775152.775226 · Cited by 1,925

On the resemblance and containment of documents

Broder, A. (2002) · Compression and Complexity of Sequences (SEQUENCES) · doi:10.1109/sequen.1997.666900 · Cited by 1,818

Extracting product features and opinions from reviews

Popescu, A.-M., & Etzioni, O. (2005) · doi:10.3115/1220575.1220618 · Cited by 1,714

A Bayesian Approach to Filtering Junk E-Mail

Sahami, M., Dumais, S., Heckerman, D. E., & Horvitz, E. (1998) · Cited by 1,212

Web data mining: exploring hyperlinks, contents, and usage data

(2012) · Choice Reviews Online · doi:10.5860/choice.49-2718 · Cited by 1,181

Mining the Web: Discovering Knowledge from Hypertext Data

Chakrabarti, S. (2002) · Thammasat University Digital Collections · Cited by 696

Rule-Based On-the-fly Web Spambot Detection Using Action Strings

Hayati, P., Potdar, V. M., Talevski, A., & Smyth, W. F. (2010) · eSpace (Curtin University) · Cited by 10

Opinion mining and opinion spam: A literature review focusing on product reviews

Sheibani, A. A. (2012) · International Symposium on Telecommunications · doi:10.1109/istel.2012.6483152 · Cited by 10

Highlighting the Fake Reviews in Review Sequence with the Suspicious Contents and Behaviours

Li, Y. (2015) · Journal of Information and Computational Science · doi:10.12733/jics20105452 · Cited by 10

Cache-conscious performance optimization for similarity search

Alabduljalil, M., Tang, X., & Yang, T. (2013) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2484028.2484077 · Cited by 10

Detecting Review Spam: Challenges and Opportunities

Ma, Y., & Li, F. (2012) · International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom) · doi:10.4108/icst.collaboratecom.2012.250640 · Cited by 10

Mechanism Design for Personalized Recommender Systems

Cai, Q., Filos-Ratsikas, A., Liu, C., & Tang, P. (2016) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2959100.2959135 · Cited by 10

Opinion Spam Detection in Social Networks

Fei, G., Li, H., & Liu, B. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804412-4.00009-7 · Cited by 11

Detecting opinion spammer groups and spam targets through community discovery and sentiment analysis

Choo, E., Yu, T., & Chi, M. (2017) · Journal of Computer Security · doi:10.3233/jcs-16941 · Cited by 11

Role of Online Data from Search Engine and Social Media in Healthcare Informatics

Nawaz, M. S., Mustafa, R. U., & Lali, M. I. U. (2017) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-5225-2607-0.ch011 · Cited by 10

A Classifier to Identify Soft Skills in a Researcher Textual Description

Azzini, A., Galimberti, A., Marrara, S., & Ratti, E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-77538-8_37 · Cited by 10

Identifying Singleton Spammers via Spammer Group Detection

Kumar, D., Shaalan, Y., Zhang, X., & Chan, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93034-3_52 · Cited by 10

Characterizing the dynamics and evolution of incentivized online reviews on Amazon

Jamshidi, S., Rejaie, R., & Li, J. (2019) · Social Network Analysis and Mining · doi:10.1007/s13278-019-0563-0 · Cited by 10

Comparing Human Computation, Machine, and Hybrid Methods for Detecting Hotel Review Spam

Harris, C. G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29374-1_7 · Cited by 10

Fake Review Detection using Principal Component Analysis and Active Learning

Muhammad, F., & Ahmed, S. (2019) · International Journal of Computer Applications · doi:10.5120/ijca2019919418 · Cited by 12

Identification of Opinion Spammers using Reviewer Reputation and Clustering Analysis

Zhong, M., Tan, L., & Qu, X. (2019) · International Journal of Computers Communications & Control · doi:10.15837/ijccc.2019.6.3704 · Cited by 10

Learning opinions in user-generated web content

Okolova, M. S., & Apalme, G. L. (2011) · Cited by 10

Network Embedding-Based Anomalous Density Searching for Multi-Group Collaborative Fraudsters Detection in Social Media

Zhu, C., Zhao, W., Li, Q., Pan, L., & Da, Q. (2019) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2019.05677 · Cited by 12

Consistent Sampling Through Extremal Process

Li, P., Li, X., Samorodnitsky, G. P., & Zhao, W. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449955 · Cited by 10

Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning

Zhong, M., Li, Z., Liu, S., Yang, B., Tan, R., & Qu, X. (2021) · Complexity · doi:10.1155/2021/9923374 · Cited by 10

UrduAI: Writeprints for Urdu Authorship Identification

Sarwar, R., & Hassan, S. (2021) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3476467 · Cited by 10

Using Inductive Transfer Learning to Improve Hotel Review Spam Detection

Crawford, M., & Khoshgoftaar, T. M. (2021) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri51335.2021.00040 · Cited by 10

Users’ interest grouping from online reviews based on topic frequency and order

Si, J., Li, Q., Qian, T., & Deng, X. (2013) · World Wide Web · doi:10.1007/s11280-013-0239-z · Cited by 10

Combining Word and Character N-Grams for Detecting Deceptive Opinions

Siagian, A. H. A. M., & Aritsugi, M. (2017) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2017.90 · Cited by 10

SPGD_HIN: Spammer Group Detection based on Heterogeneous Information Network

Bitarafan, A., & Dadkhah, C. (2019) · International Conference on Web Research (ICWR) · doi:10.1109/icwr.2019.8765274 · Cited by 10

Temporal mechanisms of polarization in online reviews

Matakos, A., & Tsaparas, P. (2016) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2016.7752286 · Cited by 10

Information Credibility in the Social Web: Contexts, Approaches, and Open IssuesPreprint

Pasi, G., & Viviani, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.09473 · Cited by 10

Catch Me If You Can — Detecting Fraudulent Online Reviews of Doctors Using Deep LearningPreprint

Shukla, A. D., Wang, W., Gao, G. G., & Agarwal, R. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3320258 · Cited by 11

Deep Learning Based Model for Fake Review Detection

Singh, D., Memoria, M., & Kumar, R. (2023) · International Conference on Advancement in Computation & Computer Technologies (InCACCT) · doi:10.1109/incacct57535.2023.10141826 · Cited by 10

NRWalk2Vec-HIN: spammer group detection based on heterogeneous information network embedding over social media

Mewada, A., & Dewang, R. K. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05537-0 · Cited by 10

SentiBERT: A Novel Approach for Fake Review Detection Incorporating Sentiment Features with Contextual Features

Mewada, A., Dewang, R. K., Goldar, P., & Maurya, S. K. (2023) · International Conference on Contemporary Computing · doi:10.1145/3607947.3607991 · Cited by 10

Robust Fake Review Detection Using Uncertainty-Aware LSTM and BERT

Zabeen, S., Hasan, A., Islam, M. F., Hossain, M. S., & Rasel, A. A. (2023) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn59264.2023.10402342 · Cited by 12

Impact of Behavioral and Textual Features on Opinion Spam Detection

Rastogi, A., & Mehrotra, M. (2018) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iccons.2018.8662912 · Cited by 9

Deceptive Opinions Detection Using New Proposed Arabic Semantic Features

Ziani, A., Azizi, N., Schwab, D., Zenakhra, D., Aldwairi, M., Chekkai, N., Zemmal, N., & Salah, M. H. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.05.067 · Cited by 9

Co-attention Based Feature Fusion Network for Spam Review Detection on Douban

Cai, H., Yu, K., Zhou, Y., & Wu, X. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-10859-w · Cited by 10

Fake review detection with label-consistent and hierarchical-relation-aware graph contrastive learning

Yao, J., Jiang, L., Shi, C., & Yan, S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112385 · Cited by 12

Automatic categorisation of comments in social news websites

Santos, I., de-la-Peña-Sordo, J., Pastor-López, I., Galán-García, P., & Bringas, P. G. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.05.061 · Cited by 9

Interpretable and effective opinion spam detection via temporal patterns mining across websites

Yuan, Y., Xie, S., Lu, C.-T., Tang, J., & Yu, P. S. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840593 · Cited by 10

Detecting Sockpuppets in Deceptive Opinion Spam

Hosseinia, M., & Mukherjee, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-77116-8_19 · Cited by 9

Online Reputation Fraud Campaign Detection in User Ratings

Xu, C., Zhang, J., & Sun, Z. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/541 · Cited by 8

Detecting Spammer Communities Using Network Structural Features

Zhou, W., Liu, M., & Zhang, Y. (2018) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-00916-8_61 · Cited by 9

Enabling a Bot with Understanding Argumentation and Providing Arguments

Galitsky, B. A. (2019) · Springer eBooks · doi:10.1007/978-3-030-04299-8_13 · Cited by 9

Cross-Domain Spam Detection in Social Media: A Survey

Dhaka, D., & Mehrotra, M. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-8300-7_9 · Cited by 8

Online Deception Detection Refueled by RealWorld Data Collection

Yao, W., Dai, Z., Huang, R., & Caverlee, J. (2017) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-049-6_102 · Cited by 9

Tracking the Human Papillomavirus Vaccine Risk Misinformation: An Explorative Study to Examine How the Misinformation Has Spread in User-Generated Content

Chin, J., Chin, C.-L., Panday, S., Ghazanfari, A., Jagadeesan, G., Wang, Z., Ontengco, A., Chang, A., Liu, B., Schwartz, A., et al. (2020) · Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care · doi:10.1177/2327857920091069 · Cited by 9

Experience

Fazzolari, M., Buccafurri, F., Lax, G., & Petrocchi, M. (2021) · Journal of Data and Information Quality · doi:10.1145/3439307 · Cited by 10

Fake Review Detection Using Machine Learning Techniques

Abhinandan, V., Aishwarya, C., & Sultana, A. (2020) · International Journal of Fog Computing · doi:10.4018/ijfc.2020070104 · Cited by 9

A cross-site comparison of online review manipulation using Benford’s law

Zhao, C., & Wang, C. A. (2021) · Electronic Commerce Research · doi:10.1007/s10660-020-09455-8 · Cited by 9

The Impact of Online Consumer Reviews on Online Hotel Booking Intention

Güler, Y. C., & Huseynov, F. (2021) · Journal of Business Research - Turk · doi:10.20491/isarder.2021.1282 · Cited by 17

Understanding Large-Scale Network Effects in Detecting Review Spammers

Rout, J. K., Sahoo, K. S., Dalmia, A., Bakshi, S., Bilal, M., & Song, H. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3243139 · Cited by 9

Hierarchical Approach to Sentiment Analysis

Kawamae, N. (2012) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc.2012.62 · Cited by 9

Diversionary Comments under Blog Posts

Wang, J., Yu, C. T., Yu, P. S., Liu, B., & Meng, W. (2015) · ACM Transactions on the Web · doi:10.1145/2789211 · Cited by 9

Potential Power and Problems in Sentiment Mining of Social Media

Wang, J., Gu, Q., & Wang, G. (2013) · International Journal of Strategic Decision Sciences · doi:10.4018/jsds.2013040102 · Cited by 9

Are user-contributed reviews community property?

Shipman, F. M., III, & Marshall, C. C. (2013) · ACM Web Science Conference (WebSci) · doi:10.1145/2464464.2464473 · Cited by 9

Detection of Political Manipulation in Online Communities through Measures of Effort and Collaboration

Lee, S. (2015) · ACM Transactions on the Web · doi:10.1145/2767134 · Cited by 10

Identifying outlier opinions in an online intelligent argumentation system

Arvapally, R. S., Liu, X. F., Nah, F. F., & Jiang, W. (2017) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4107 · Cited by 10

Open Set Text Classification Using Neural Networks

Venkataram, V. (2018) · Digital Collections of Colorado (Colorado State University) · Cited by 8

Exponential Distribution model for Review Spam Detection

Biradar, J. G. (2017) · International Journal of Advanced Research in Computer Science · doi:10.26483/ijarcs.v8i3.3129 · Cited by 9

Stacking GA 2 M for inherently interpretable fraudulent reviewer identification by fusing target and non-target features

Zhang, W., Zhang, X., Chen, J., Li, J., & Ma, Z. (2024) · International Journal of General Systems · doi:10.1080/03081079.2024.2384404 · Cited by 8

Heterogeneous graph representation learning via mutual information estimation for fraud detection

Zhang, Z., Su, X., Wu, J., Tessone, C. J., & Liao, H. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.104046 · Cited by 8

A self-supervised transformer framework for cross-domain fraudulent review detection (CROSS-FRD)

Al‐Yousef, A., Mohawesh, R., Saifan, A. A., & Al-Shannaq, M. A. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-08061-5 · Cited by 10

Opinioned Post Detection in Sina Weibo

Lv, Y., Liu, J., Chen, H., Mi, J., Liu, M., & Zheng, Q. (2017) · IEEE Access · doi:10.1109/access.2017.2679227 · Cited by 8

A Supervised Framework for Review Spam Detection in the Persian Language

Basiri, M. E., Safarian, N., & Farsani, H. K. (2019) · International Conference on Web Research (ICWR) · doi:10.1109/icwr.2019.8765275 · Cited by 8

Fitness-Based Grey Wolf Optimizer Clustering Method for Spam Review Detection

Shringi, S., Sharma, H., & Suthar, D. L. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/6499918 · Cited by 9

A deep matching model for detecting reviews mismatched with products in e-commerce

Qiu, J., & Wang, S. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109624 · Cited by 8

Classifying deceptive reviews for the cultural heritage domain: A lexicon-based approach for the Italian language

Guarasci, R., Catelli, R., & Esposito, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124131 · Cited by 9

Detecting the spam review using tri-training

Chengzhang, J., & Kang, D.-K. (2015) · International Conference on Advanced Communication Technology · doi:10.1109/icact.2015.7224822 · Cited by 8

An Effective Approach to Rank Reviews Based on Relevance by Weighting Method

Shri, J. M. R., & Subramaniyaswamy, V. (2015) · Indian Journal of Science and Technology · doi:10.17485/ijst/2015/v8i11/61768 · Cited by 8

A faceted characterization of the opinion mining landscape

Arora, R., & Srinivasa, S. (2014) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets.2014.6734936 · Cited by 8

The Freshman Handbook: A Hint for Server Placement in Online Social Network Services

Zhang, Y., Li, D., & Tatipamula, M. (2012) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads.2012.85 · Cited by 8

Untitled work

(2015) · International Conference on Information Systems · Cited by 8

Constructing and Evaluating a Novel Crowdsourcing-based Paraphrased Opinion Spam Dataset

Kim, S., Lee, S., Park, D., & Kang, J. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052607 · Cited by 9

Detection of False Online Advertisements with DCNN

Huang, H., Wen, Y.-W., & Chen, H. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3054233 · Cited by 8

Temporal and Sentimental Analysis of A Real Case of Fake Reviews in Taiwan

Wang, C., Day, M.-Y., Chen, C.-C., & Liou, J.-W. (2017) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3110025.3116206 · Cited by 8

Multiple Criteria Fake Reviews Detection Using Belief Function Theory

Khalifa, M. B., Elouedi, Z., & Lefèvre, É. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-16657-1_29 · Cited by 8

An Inferable Representation Learning for Fraud Review Detection with Cold-start Problem

Li, Q., Wu, Q., Zhu, C., Zhang, J., & Zhao, W. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852437 · Cited by 9

Fusion Convolutional Attention Network for Opinion Spam Detection

Li, J., Ma, Q., Yuan, C., Zhou, W. J., Han, J., & Hu, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-36708-4_19 · Cited by 8

An Evidential Spammer Detection based on the Suspicious Behaviors' Indicators

Khalifa, M. B., Elouedi, Z., & Lefèvre, É. (2020) · International Multi-Conference on Organization of Knowledge and Advanced Technologies (OCTA) · doi:10.1109/octa49274.2020.9151805 · Cited by 8

Opinion Spam Classification on Steam Review using Support Vector Machine with Lexicon-Based Features

Taqiuddin, R., Bachtiar, F. A., & Purnomo, W. (2021) · Kinetik Game Technology Information System Computer Network Computing Electronics and Control · doi:10.22219/kinetik.v6i4.1323 · Cited by 8

Natural Language Processing for Social Media, Third Edition

Farzindar, A., & Inkpen, D. Z. (2020) · Synthesis lectures on human language technologies · doi:10.2200/s00999ed3v01y202003hlt046 · Cited by 8

Roman Urdu Fake Reviews Detection Using Stacked LSTM Architecture

Hayat, U., Saeed, A., Vardag, M. H. K., Ullah, M. F., & Iqbal, N. (2022) · SN Computer Science · doi:10.1007/s42979-022-01385-6 · Cited by 9

A critical analysis of the state-of-the-art on automated detection of deceptive behavior in social media

Song, L., Zhang, W., Liao, S. S., & Kwok, R. C.-W. (2012) · Pacific Asia Conference on Information Systems · Cited by 8

Detecting Product Review Spammers using Activity Model

Jiang, B., Cao, R. H., & Chen, B. (2013) · International Conference on Advanced Computer Science and Electronics Information (ICACSEI) · doi:10.2991/icacsei.2013.155 · Cited by 8

Mining Information of Anonymous User on a Social Network Service

Cho, K. S., Yoon, J. Y., Kim, I. J., Lim, J. Y., Kim, S. K., & Kim, U. M. (2011) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2011.19 · Cited by 8

A Unified Framework for Opinion Retrieval

Miao, Q., Li, Q., & Dai, R. (2008) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wiiat.2008.126 · Cited by 8

Analysis of Cyber Army’s Behaviours on Web Forum for Elect Campaign

Ko, M.-C., & Chen, H. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-28940-3_32 · Cited by 8

CoFea: A Novel Approach to Spam Review Identification Based on Entropy and Co-Training

Zhang, W., Bu, C., Yoshida, T., & Zhang, S. (2016) · Entropy · doi:10.3390/e18120429 · Cited by 8

A Novel Model for Opinion Spam Detection Based on Multi-Iteration Network Structure

Noekhah, S., Salim, N. B., & Zakaria, N. H. (2018) · Advanced Science Letters · doi:10.1166/asl.2018.10765 · Cited by 8

Opinion Spam Detection through User Behavioral Graph Partitioning Approach

Manaskasemsak, B., Chanmakho, C., Klainongsuang, J., & Rungsawang, A. (2019) · International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI) · doi:10.1145/3325773.3325783 · Cited by 8

Dynamic Slide Window-Based Feature Scoring and Extraction for On-Line Rumor Detection with CNN

Yang, H., Xu, Z., Liu, L., Guo, M., & Zhang, Y. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761288 · Cited by 9

Catching Them Red-Handed

Han, X., Yaraghi, N., & Gopal, R. D. (2019) · ACM Transactions on Management Information Systems · doi:10.1145/3325522 · Cited by 8

Using Data Analytics to Filter Insincere Posts from Online Social Networks A Case Study: Quora Insincere Questions

Al-Ramahi, M. A., & Alsmadi, I. M. (2020) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2020.304 · Cited by 8

Analyzing and Detecting Adversarial Spam on a Large-scale Online APP Review System

Wang, J., Wen, R., Wu, C., & Xiong, J. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366424.3383756 · Cited by 8

An unsupervised approach to detect review spam using duplicates of images, videos and Chinese texts

Li, J., Zhang, P., & Yang, L. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101186 · Cited by 8

Review Spammer Detection by using Behaviors Based Scoring Methods

(2014) · International Conference on Advances in Engineering and Technology (ICAET'2014) March 29-30, 2014 Singapore · doi:10.15242/iie.e0314158 · Cited by 9

Analyzing Sentiments for Generating Opinions (ASGO)-A New Approach

Zol, S., & Mulay, P. (2015) · Indian Journal of Science and Technology · doi:10.17485/ijst/2015/v8is4/62327 · Cited by 8

Crowdsourcing Consumer Confidence: How to Regulate Online Rating and Review Systems in the Collaborative EconomyPreprint

Busch, C. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2799489 · Cited by 8

Exploring online deviant behaviour: a study on the impact of internet anonymity on tourists’ exaggerated comments

Guan, X., Gong, J., Zheng, Y., & Huan, T.-C. T. C. (2024) · Current Issues in Tourism · doi:10.1080/13683500.2024.2353329 · Cited by 7

A metadata-aware detection model for fake restaurant reviews based on multimodal fusion

Jian, Y., Chen, X., Wang, X., Liu, Y., Chen, X., Lan, X., Wang, W., & Wang, H. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10647-8 · Cited by 8

Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks

Phukon, P., Ποτίκας, Π., & Potika, K. (2025) · Applied Sciences · doi:10.3390/app15073771 · Cited by 11

Attention-based BiLSTM with positional embeddings for fake review detection

Chen, J., Zhang, T. Y., Yan, Z., Zheng, Z., Zhang, W., & Zhang, J. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01130-9 · Cited by 17

Analyzing and Detecting Review Spam

Jindal, N., & Liu, B. (2007) · Proceedings · doi:10.1109/icdm.2007.68 · Cited by 226

Collective Opinion Spam Detection: Bridging Review Networks and Metadata

Rayana, S., & Akoglu, L. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783370 · Cited by 604

Opinion Spam Detection in Online Reviews

Rastogi, A., & Mehrotra, M. (2017) · Journal of Information & Knowledge Management · doi:10.1142/s0219649217500368 · Cited by 51

Opinion mining and opinion spam: A literature review focusing on product reviews

Sheibani, A. A. (2012) · International Symposium on Telecommunications · doi:10.1109/istel.2012.6483152 · Cited by 10

Opinion Spam Detection

Bensouda, N., Fkihi, S. E., & Faizi, R. (2018) · International Conference on Learning and Optimization Algorithms: Theory and Applications (LOPAL) · doi:10.1145/3230905.3230922 · Cited by 4