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

Sentiment Analysis for Scraping of Product Reviews from Multiple Web Pages Using Machine Learning Algorithms

Suganya, E., & Vijayarani, S. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-16660-1_66 · Cited by 21

Supporting the construction of affective product taxonomies from online customer reviews: an affective-semantic approach

Wang, W. M., Tian, Z., Li, Z., Wang, J., Barenji, A. V., & Cheng, M. (2019) · Journal of Engineering Design · doi:10.1080/09544828.2019.1642460 · Cited by 21

The Effects of Negative Online Reviews on Consumer Perception, Attitude and Purchase Intention: Experimental Investigation of the Amount, Quality, and Presentation Order of eWOM

Liao, H.-L., Huang, Z., & Liu, S.-H. (2021) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3426883 · Cited by 27

Graph-Aware Deep Fusion Networks for Online Spam Review Detection

Li, H., Xu, G., Jameel, S., Wang, X., & Chen, H. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3189813 · Cited by 21

Comprehensive examination of online reviews divergence over time and platform types

Kirilenko, A., Stepchenkova, S. O., Gromoll, R., & Jo, Y. (2023) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2023.103647 · Cited by 27

An Enhanced Random Forest (ERF)-based Machine Learning Framework for Resampling, Prediction, and Classification of Mobile Applications using Textual Features

Hussain, S., Sarwar, N., Ali, A., Khan, H., Din, I., Alqahtani, A. M., Shabir, M., & Ali, A. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.9148 · Cited by 21

Overcoming the challenge for text classification in the open world

Doan, T., & Kalita, J. K. (2017) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2017.7868366 · Cited by 21

Fake Review Detection Based on PU Learning and Behavior Density

He, D., Pan, M., Hong, K., Cheng, Y., Chan, S., Liu, X., & Guizani, N. (2020) · IEEE Network · doi:10.1109/mnet.001.1900542 · Cited by 21

Monitoring online reviews for reputation fraud campaigns

Wang, Z., & Chen, Q. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105685 · Cited by 21

External validity of sentiment mining reports: can current methods identify demographic biases, event biases, and manipulation of reviews?

Wijnhoven, F., & Bloemen, O. (2014) · Decision Support Systems · doi:10.1016/j.dss.2013.12.005 · Cited by 21

Identification of Fake Reviews Using New Set of Lexical and Syntactic Features

Dewang, R. K., & Singh, A. K. (2015) · International Conference on Computer and Communication Technology · doi:10.1145/2818567.2818589 · Cited by 21

Semantic technologies in idea management systems: a model for interoperability, linking and filtering

Westerski, A. (2013) · doi:10.20868/upm.thesis.14622 · Cited by 21

Detecting opinion spam and fake news using n-gram analysis and semantic similarity

Ahmed, H. (2017) · UVic’s Research and Learning Repository (University of Victoria) · Cited by 21

Dataset of Fake News Detection and Fact Verification: A Survey

Murayama, T. (2021) · arXiv · doi:10.48550/arxiv.2111.03299 · Cited by 40

Emerging Sources, Formats, Channels, Devices, and Audiences in Modern eWOM Communications

Filieri, R., Christodoulides, G., & Nicolau, J. L. (2025) · Psychology and Marketing · doi:10.1002/mar.22239 · Cited by 23

Domain Independent Sentiment Classification with Many Lexicons

Ohana, B., Tierney, B., & Delany, S. J. (2011) · International Conference on Advanced Information Networking and Applications Workshops (WAINA) · doi:10.1109/waina.2011.103 · Cited by 20

Semi-Supervised Fake Reviews Detection based on AspamGAN

Jing-Yu, C., & Wang, Y. (2022) · Journal of Artificial Intelligence and Capsule Networks · doi:10.36548/jaicn.2022.1.002 · Cited by 20

Detection of near-duplicate user generated contents

Vallés, E., & Rosso, P. (2011) · International Workshop on Search and Mining User-Generated Contents (SMUC) · doi:10.1145/2065023.2065031 · Cited by 20

Understanding the process of writing fake online reviews

Banerjee, S., & Chua, A. Y. K. (2014) · International Conference on Digital Information Management · doi:10.1109/icdim.2014.6991395 · Cited by 20

Information Retrieval on the Blogosphere

Santos, R. L. T. (2012) · now publishers, Inc. eBooks · doi:10.1561/9781601985699 · Cited by 20

Mathematical Modeling and Analysis of Product Rating with Partial Information

Xie, H. L., & Lui, J. C. S. (2015) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2700386 · Cited by 20

Exploratory Study on Anchoring: Fake Vote Counts in Consumer Reviews Affect Judgments of Information Quality

Nakayama, M., & Wan, Y. (2016) · Journal of theoretical and applied electronic commerce research · doi:10.4067/s0718-18762017000100002 · Cited by 22

Big Text advantages and challenges: classification perspective

Sokolova, M. (2017) · International Journal of Data Science and Analytics · doi:10.1007/s41060-017-0087-5 · Cited by 20

Sentiment and Emotion Based Text Representation for Fake Reviews Detection

Melleng, A., Loughrey, A.-J., & Deepak, P. (2019) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-056-4_087 · Cited by 22

Detecting and Analysing Fake Opinions Using Artificial Intelligence Algorithms

Al-Adhaileh, M. H., & Alsaade, F. W. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.021225 · Cited by 20

The best answers? think twice

Chen, C., Wu, K., Srinivasan, V., & Bharadwaj, R. K. (2013) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2492517.2492553 · Cited by 20

A unified model for unsupervised opinion spamming detection incorporating text generality

Xu, Y., Shi, B., Tian, W., & Lam, W. P. (2015) · International Conference on Artificial Intelligence · Cited by 18

Online Spam Review Detection: A Survey of Literature

Li, H., Wang, X., Chen, H., & Xu, G. (2022) · Human-Centric Intelligent Systems · doi:10.1007/s44230-022-00001-3 · Cited by 24

Quality-biased Ranking of Short Texts in Microblogging Services

Huang, M., Yang, Y., & Zhu, X. (2011) · Cited by 19

Deceptive Opinion Spam Detection Using Deep Level Linguistic Features

Chen, C., Zhao, H., & Yang, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25207-0_43 · Cited by 18

A Framework for Fake Review Annotation

Shojaee, S., Azman, A., Murad, M. A. A., Sharef, N. M., & Sulaiman, N. (2015) · international conference on Modelling and simulation · doi:10.1109/uksim.2015.38 · Cited by 19

Natural Language Processing for Social Media, Second Edition

Farzindar, A., & Inkpen, D. Z. (2017) · Synthesis lectures on human language technologies · doi:10.2200/s00809ed2v01y201710hlt038 · Cited by 19

Spiral of Silence in Recommender Systems

Liu, D., Lin, C., Zhang, Z., Xiao, Y., & Tong, H. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291003 · Cited by 18

Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A Survey

Guo, B., Liu, Y., Ouyang, Y., Zheng, V. W., Zhang, D., & Yu, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2901027 · Cited by 19

Detection of Fake Reviews Using Group Model

Li, Y., Wang, F., Zhang, S., & Niu, X. (2020) · Mobile Networks and Applications · doi:10.1007/s11036-020-01688-z · Cited by 20

Social Network Spam Detection Based on ALBERT and Combination of Bi-LSTM with Self-Attention

Xu, G., Zhou, D., & Liu, J. (2021) · Security and Communication Networks · doi:10.1155/2021/5567991 · Cited by 19

Adaptive big data analytics for deceptive review detection in online social media

Zhang, W., Lau, R. Y. K., & Li, C. (2014) · Journal of the Association for Information Systems · Cited by 19

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Lai, C. L., Xu, K., Lau, R. Y. K., Li, Y., & Song, D. (2010) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2010.30 · Cited by 21

AI vs. human: A large-scale analysis of AI-generated fake reviews, human-generated fake reviews and authentic reviews

Zhao, Y., Tang, S., Zhang, H., & Lyu, L. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104400 · Cited by 28

A Survey On Spam URLs Detection In Twitter

Daffa, W., Bamasag, O. O., & AlMansour, A. A. (2018) · International Conference on Computer Applications & Information Security (ICCAIS) · doi:10.1109/cais.2018.8441975 · Cited by 17

Scarcity-aware spam detection technique for big data ecosystem

Park, W. H., Siddiqui, I. F., Chakraborty, C., Qureshi, N. M. F., & Shin, D. R. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.03.021 · Cited by 18

EUPHORIA: A neural multi-view approach to combine content and behavioral features in review spam detection

Andresini, G., Iovine, A., Gasbarro, R., Lomolino, M., de Gemmis, M., & Appice, A. (2022) · Journal of Computational Mathematics and Data Science · doi:10.1016/j.jcmds.2022.100036 · Cited by 19

Sentiment analysis in financial markets A framework to utilize the human ability of word association for analyzing stock market news reports

Uhr, P., Zenkert, J., & Fathi, M. (2014) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2014.6974028 · Cited by 18

A fuzzy system-based approach to estimate the importance of online customer reviews

de Sousa, R. F., Rabêlo, R. A. L., & Moura, R. S. (2015) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2015.7337914 · Cited by 18

Sentiment Analysis Based Online Restaurants Fake Reviews Hype Detection

Deng, X., & Chen, R. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11119-3_1 · Cited by 18

Credibility in Information Retrieval

Ginsca, A., Popescu, A., & Lupu, M. (2015) · now publishers, Inc. eBooks · doi:10.1561/9781680830873 · Cited by 18

A Multi-criteria Decision Making Approach for the Assessment of Information Credibility in Social Media

Viviani, M., & Pasi, G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-52962-2_17 · Cited by 20

Automatic labeling of mobile apps by the type of psychological needs they satisfy

Sun, Z., Ji, Z., Zhang, P., Chen, C., Qian, X., Du, X., & Wan, Q. (2017) · Telematics and Informatics · doi:10.1016/j.tele.2017.03.001 · Cited by 18

Identifying Humor in Reviews using Background Text Sources

Morales, A., & Zhai, C. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1051 · Cited by 16

A worker clustering-based approach of label aggregation under the belief function theory

Abassi, L., & Boukhris, I. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1209-z · Cited by 18

Lifelong Machine Learning, Second Edition

Chen, Z., & Liu, B. (2018) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00832ed1v01y201802aim037 · Cited by 160

Extracting user requirements from online reviews for product design: A supportive framework for designers

Anh, K. Q., Nagai, Y., & Minh, N. L. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-179352 · Cited by 18

Text-Based User-kNN: Measuring User Similarity Based on Text Reviews

Terzi, M., Rowe, M., Ferrario, M. A., & Whittle, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-08786-3_17 · Cited by 18

Information Systems for “Wicked Problems”

Schoder, D., Putzke, J., Metaxas, P., Gloor, P. A., & Fischbach, K. (2014) · Business & Information Systems Engineering · doi:10.1007/s12599-013-0303-3 · Cited by 19

A recommender system for the TV on the web: integrating unrated reviews and movie ratings

Peleja, F., Dias, P. I. R. C., Martins, F., & Magalhães, J. (2013) · Multimedia Systems · doi:10.1007/s00530-013-0310-8 · Cited by 18

HelpMeter: A Nonlinear Model for Predicting the Helpfulness of Online Reviews

Liu, Y., Huang, X., An, A., & Yu, X. (2008) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wiiat.2008.299 · Cited by 18

Finding Deceptive Opinion Spam by Correcting the Mislabeled Instances

Ren, Y., Ji, D., Yin, L., & Zhang, H. (2015) · Chinese Journal of Electronics · doi:10.1049/cje.2015.01.009 · Cited by 18

Think Outside the Dataset

Nilizadeh, S., Aghakhani, H., Gustafson, E., Kruegel, C., & Vigna, G. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313647 · Cited by 18

Wide-Ranging Review Manipulation Attacks

Kaghazgaran, P., Alfifi, M., & Caverlee, J. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3358034 · Cited by 21

A categorization scheme for socialbot attacks in online social networksPreprint

Mitter, S., Wagner, C., & Strohmaier, M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1402.6288 · Cited by 22

Learning-Based Model to Fight against Fake Like Clicks on Instagram Posts

Thejas, G. S., Soni, J., Chandna, K., Iyengar, S. S., Sunitha, N. R., & Prabakar, N. (2019) · IEEE SoutheastCon · doi:10.1109/southeastcon42311.2019.9020533 · Cited by 17

Detecting Arabic Spam Reviews in Social Networks Based on Classification Algorithms

Najadat, H., Alzubaidi, M. A., & Qarqaz, I. (2021) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3476115 · Cited by 17

Supervised N-gram topic model

Kawamae, N. (2014) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2556195.2559895 · Cited by 17

Detecting cooperative and organized spammer groups in micro-blogging community

Dang, Q., Zhou, Y., Gao, F., & Sun, Q. (2016) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0479-5 · Cited by 16

Exploring Latent Semantic Factors to Find Useful Product Reviews

Mukherjee, S., Popat, K., & Weikum, G. (2017) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611974973.54 · Cited by 17

Finding Rotten Eggs: A Review Spam Detection Model using Diverse Feature Sets

Akram, A. U., Khan, H. U., Iqbal, S., Iqbal, T., Munir, E. U., & Shafi, M. (2018) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2018.10.026 · Cited by 17

Unsupervised User Behavior Representation for Fraud Review Detection with Cold-Start Problem

Li, Q., Wu, Q., Zhu, C., Zhang, J., & Zhao, W. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16148-4_18 · Cited by 17

Identifying ground truth in opinion spam: an empirical survey based on review psychology

Li, J., Wang, X., Yang, L., Zhang, P., & Yang, D. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01764-7 · Cited by 18

Explainable AI for Multimodal Credibility Analysis: Case Study of Online Beauty Health (Mis)-Information

Wagle, V., Kaur, K., Kamat, P. V., Patil, S., & Kotecha, K. V. (2021) · IEEE Access · doi:10.1109/access.2021.3111527 · Cited by 17

A Deceptive Reviews Detection Method Based on Multidimensional Feature Construction and Ensemble Feature Selection

Li, S., Zhong, G., Jin, Y., Wu, X., Zhu, P., & Wang, Z. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3144013 · Cited by 18

An Evolutionary Computation Approach for Twitter Bot Detection

Rovito, L., Bonin, L., Manzoni, L., & Lorenzo, A. D. (2022) · Applied Sciences · doi:10.3390/app12125915 · Cited by 18

A Case-Based Approach to Cross Domain Sentiment Classification

Ohana, B., Delany, S. J., & Tierney, B. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32986-9_22 · Cited by 17

Detecting Deceptive Opinion Spam using Linguistics, Behavioral and Statistical Modeling

Mukherjee, A. (2015) · doi:10.3115/v1/p15-5007 · Cited by 17

Multi-view Ensemble Learning Using Rough Set Based Feature Ranking for Opinion Spam Detection

Saini, M., Verma, S., & Sharan, A. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-0341-8_1 · Cited by 18

Secure and smart autonomous multi-robot systems for opinion spammer detection

Xu, G., Hu, M., & Ma, C. (2021) · Information Sciences · doi:10.1016/j.ins.2021.07.072 · Cited by 16

Explainable knowledge integrated sequence model for detecting fake online reviews

Han, S. Y., Wang, H., Li, W., Zhang, H., & Zhuang, L. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03822-8 · Cited by 18

Predicting Credibility of Online Reviews: An Integrated Approach

Abedin, E., Mendoza, A., Akbarighatar, P., & Karunasekera, S. A. (2024) · IEEE Access · doi:10.1109/access.2024.3383846 · Cited by 18

Credible Review Detection with Limited Information Using Consistency Features

Mukherjee, S., Dutta, S., & Weikum, G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46227-1_13 · Cited by 17

Deception Detection and Opinion Spam

Rosso, P., & Cagnina, L. (2017) · Socio-affective computing · doi:10.1007/978-3-319-55394-8_8 · Cited by 16

Improving detection of untrustworthy online reviews using ensemble learners combined with feature selection

Heredia, B., Khoshgoftaar, T. M., Prusa, J. D., & Crawford, M. (2017) · Social Network Analysis and Mining · doi:10.1007/s13278-017-0456-z · Cited by 16

Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features

Schulder, M., Wiegand, M., Ruppenhofer, J., & Roth, B. (2017) · Publication Server of the Institute for German Language (Institute for German Language) · doi:10.5281/zenodo.3365608 · Cited by 16

Introducing a Lexicon of Verbal Polarity Shifters for English

Schulder, M., Wiegand, M., Ruppenhofer, J., & Köser, S. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3d9x4memsk7e · Cited by 16

Decomposing TripAdvisor: Detecting Potentially Fraudulent Hotel Reviews in the Era of Big Data

Harris, C. G. (2018) · IEEE International Conference on Big Knowledge (ICBK) · doi:10.1109/icbk.2018.00040 · Cited by 18

DCWord: A Novel Deep Learning Approach to Deceptive Review Identification by Word Vectors

Zhang, W., Wang, Q., Li, X., Yoshida, T., & Li, J. (2019) · Journal of Systems Science and Systems Engineering · doi:10.1007/s11518-019-5438-4 · Cited by 16

Fake opinion detection: how similar are crowdsourced datasets to real data?

Fornaciari, T., Cagnina, L., Rosso, P., & Poesio, M. (2020) · Language Resources and Evaluation · doi:10.1007/s10579-020-09486-5 · Cited by 17

IFSpard: An Information Fusion-based Framework for Spam Review Detection

Zhu, Y., Liu, H., Du, Y., & Wu, Z. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449920 · Cited by 16

DFraud³: Multi-Component Fraud Detection Free of Cold-Start

Shehnepoor, S., Togneri, R. B., Liu, W., & Bennamoun, M. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3081258 · Cited by 16

Do Reviewers’ Words and Behaviors Help Detect Fake Online Reviews and Spammers? Evidence From a Hierarchical Model

Le, T.-K.-H., Li, Y., & Li, S.-T. (2022) · IEEE Access · doi:10.1109/access.2022.3167511 · Cited by 18

Mining Sequential Patterns: A Context-Aware Approach

Rabatel, J., Bringay, S., & Pascal Poncelet, B. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-35855-5_2 · Cited by 16

An interpretation of sentiment analysis for enrichment of Business Intelligence

Singh, B., Kushwaha, N., & Vyas, O. P. (2016) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2016.7847950 · Cited by 18

Hybrid Optimization Algorithm of Improved Binary Particle Swarm Optimization (iBPSO) And Cuckoo Search for Review Spam Detection

Rajamohana, S. P., & Umamaheswari, K. (2017) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3055635.3056647 · Cited by 16

Multi-Aspect Feature Based Neural Network Model in Detecting Fake Reviews

Luo, N., Deng, H., Zhao, L., Liu, Y., Wang, X., & Tan, Z. (2017) · International Conference on Information Science and Control Engineering · doi:10.1109/icisce.2017.106 · Cited by 16

RST-style Discourse Parsing and Its Applications in Discourse Analysis

Feng, V. W. (2015) · TSpace (University of Toronto) · Cited by 16

FairJudge: Trustworthy User Prediction in Rating PlatformsPreprint

Kumar, S., Hooi, B., Makhija, D., Kumar, M., Faloutsos, C., & Subrahamanian, V. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10545 · Cited by 16

Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNNPreprint

Zhao, S., Xu, Z., Liu, L., & Guo, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.09181 · Cited by 16

Fraud De-Anonymization for Fun and Profit

Hernandez, N., Rahman, M., Recabarren, R., & Carbunar, B. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3243734.3243770 · Cited by 16

Advancing E-Commerce Authenticity: A Novel Fusion Approach Based on Deep Learning and Aspect Features for Detecting False Reviews

Abd-Alhalem, S. M., Ali, H. A., Soliman, N. F., Algarni, A. D., & Salem, H. (2024) · IEEE Access · doi:10.1109/access.2024.3435916 · Cited by 19

A Hybrid Deep Learning Approach for Enhanced Sentiment Classification and Consistency Analysis in Customer Reviews

Sorour, S. E., Alojail, A., El-Shora, A. E., Amin, A. E., & Abohany, A. A. (2024) · Mathematics · doi:10.3390/math12233856 · Cited by 22

Trust-Aware Review Spam Detection

Xue, H., Li, F., Seo, H., & Pluretti, R. (2015) · doi:10.1109/trustcom-bigdatase-ispa.2015.440 · Cited by 16

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