{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:45:09Z","timestamp":1778168709926,"version":"3.51.4"},"reference-count":86,"publisher":"Springer Science and Business Media LLC","issue":"1-2","license":[{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"AUSTRALIA RESARCH COUNCIL","award":["DP200101374"],"award-info":[{"award-number":["DP200101374"]}]},{"name":"AUSTRALIA RESARCH COUNCIL","award":["LP170100891"],"award-info":[{"award-number":["LP170100891"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Hum-Cent Intell Syst"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The increasingly developed online platform generates a large amount of online reviews every moment, e.g., Yelp and Amazon. Consumers gradually develop the habit of reading previous reviews before making a decision of buying or choosing various products. Online reviews play an vital part in determining consumers\u2019 purchase choices in e-commerce, yet many online reviews are intentionally created to confuse or mislead potential consumers. Moreover, driven by product reputations and merchants\u2019 profits, more and more spam reviews were inserted into online platform. This kind of reviews can be positive, negative or neutral, but they had common features: misleading consumers or damaging reputations. In the past decade, many people conducted research on detecting spam reviews using statistical or deep learning method with various datasets. In view of that, this article first introduces the task of spam online reviews detection and makes a common definition of spam reviews. Then, we comprehensively conclude the existing method and available datasets. Third, we summarize the existing network-based approaches in dealing with this task and propose some direction for future research.<\/jats:p>","DOI":"10.1007\/s44230-022-00001-3","type":"journal-article","created":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T18:03:56Z","timestamp":1651773836000},"page":"14-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Online Spam Review Detection: A Survey of Literature"],"prefix":"10.1007","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4637-8733","authenticated-orcid":false,"given":"Li","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianzhi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongxu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,5]]},"reference":[{"issue":"563","key":"1_CR1","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1111\/j.1468-0297.2012.02512.x","volume":"122","author":"M Anderson","year":"2012","unstructured":"Anderson M, Magruder J. Learning from the crowd: regression discontinuity estimates of the effects of an online review database. Econ J. 2012;122(563):957\u201389.","journal-title":"Econ J"},{"key":"1_CR2","unstructured":"Luca M. Reviews, reputation, and revenue: the case of yelp. com. In: Com (March 15, 2016). Harvard Business School NOM Unit Working Paper,2016; no. 12-016."},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Park C-H, Kim Y-G. Identifying key factors affecting consumer purchase behavior in an online shopping context. Int J Retail Distrib Manage. 2003.","DOI":"10.1108\/09590550310457818"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Jindal N, Liu B. Opinion spam and analysis. In: Proceedings of the 2008 international conference on web search and data mining, 2008; pp. 219\u201330.","DOI":"10.1145\/1341531.1341560"},{"key":"1_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2020.113280","volume":"132","author":"Y Wu","year":"2020","unstructured":"Wu Y, Ngai EW, Wu P, Wu C. Fake online reviews: literature review, synthesis, and directions for future research. Decis Support Syst. 2020;132: 113280.","journal-title":"Decis Support Syst"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Li A, Qin Z, Liu R, Yang, Y, Li D. Spam review detection with graph convolutional networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019; pp. 2703\u201311.","DOI":"10.1145\/3357384.3357820"},{"issue":"4","key":"1_CR7","first-page":"1","volume":"2","author":"RY Lau","year":"2012","unstructured":"Lau RY, Liao S, Kwok RC-W, Xu K, Xia Y, Li Y. Text mining and probabilistic language modeling for online review spam detection. ACM Trans Manage Inf Syst (TMIS). 2012;2(4):1\u201330.","journal-title":"ACM Trans Manage Inf Syst (TMIS)"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Ott M, Cardie C, Hancock J. Estimating the prevalence of deception in online review communities. In: Proceedings of the 21st international conference on World Wide Web, 2012; pp. 201\u201310.","DOI":"10.1145\/2187836.2187864"},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.fss.2014.01.015","volume":"258","author":"V L\u00f3pez","year":"2015","unstructured":"L\u00f3pez V, Del R\u00edo S, Ben\u00edtez JM, Herrera F. Cost-sensitive linguistic fuzzy rule based classification systems under the mapreduce framework for imbalanced big data. Fuzzy Sets Syst. 2015;258:5\u201338.","journal-title":"Fuzzy Sets Syst"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Fei G, Mukherjee A, Liu B, Hsu M, Castellanos M, Ghosh R. Exploiting burstiness in reviews for review spammer detection. In: Proceedings of the International AAAI Conference on Web and Social Media, 2013; vol. 7, no. 1.","DOI":"10.1609\/icwsm.v7i1.14400"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Mukherjee A, Liu B, Glance N. Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st international conference on World Wide Web, 2012; pp. 191\u2013200.","DOI":"10.1145\/2187836.2187863"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Wang C-C, Day M-Y, Chen C-C, Liou J-W. Detecting spamming reviews using long short-term memory recurrent neural network framework. In: Proceedings of the 2nd International Conference on E-commerce, E-Business and E-Government, 2018; pp. 16\u201320.","DOI":"10.1145\/3234781.3234794"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Weng H, Ji S, Duan F, Li Z, Chen J, He Q, Wang T. Cats: cross-platform e-commerce fraud detection. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 2019; pp. 1874\u201385.","DOI":"10.1109\/ICDE.2019.00203"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Rayana S, Akoglu L. Collective opinion spam detection: bridging review networks and metadata. In: Proceedings of the 21th acm sigkdd international conference on knowledge discovery and data mining, 2015; pp. 985\u201394.","DOI":"10.1145\/2783258.2783370"},{"issue":"7","key":"1_CR15","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1109\/TIFS.2017.2675361","volume":"12","author":"S Shehnepoor","year":"2017","unstructured":"Shehnepoor S, Salehi M, Farahbakhsh R, Crespi N. Netspam: a network-based spam detection framework for reviews in online social media. IEEE Trans Inf Forensics Secur. 2017;12(7):1585\u201395.","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Wang X, Liu K, Zhao J. Handling cold-start problem in review spam detection by jointly embedding texts and behaviors. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2017; pp. 366\u201376.","DOI":"10.18653\/v1\/P17-1034"},{"key":"1_CR17","doi-asserted-by":"publisher","first-page":"42934","DOI":"10.1109\/ACCESS.2019.2908495","volume":"7","author":"Y Ren","year":"2019","unstructured":"Ren Y, Ji D. Learning to detect deceptive opinion spam: a survey. IEEE Access. 2019;7:42934\u201345.","journal-title":"IEEE Access"},{"issue":"2","key":"1_CR18","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1007\/s10462-019-09697-5","volume":"53","author":"DU Vidanagama","year":"2020","unstructured":"Vidanagama DU, Silva TP, Karunananda AS. Deceptive consumer review detection: a survey. Artif Intell Rev. 2020;53(2):1323\u201352.","journal-title":"Artif Intell Rev"},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Lai C, Xu K, Lau RY, Li Y, Jing L. Toward a language modeling approach for consumer review spam detection. In: 2010 IEEE 7th International Conference on E-Business Engineering. IEEE, 2010; pp. 1\u20138.","DOI":"10.1109\/ICEBE.2010.47"},{"issue":"5","key":"1_CR20","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1002\/int.21844","volume":"32","author":"M Viviani","year":"2017","unstructured":"Viviani M, Pasi G. Quantifier guided aggregation for the veracity assessment of online reviews. Int J Intell Syst. 2017;32(5):481\u2013501.","journal-title":"Int J Intell Syst"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Fontanarava J, Pasi G, Viviani M. An ensemble method for the credibility assessment of user-generated content. In: Proceedings of the International Conference on Web Intelligence, 2017; pp. 863\u20138.","DOI":"10.1145\/3106426.3106464"},{"key":"1_CR22","unstructured":"Noekhah S, Fouladfar E, Salim N, Ghorashi SH, Hozhabri AA. A novel approach for opinion spam detection in e-commerce. In: Proceedings of the 8th IEEE international conference on E-commerce with focus on E-trust, 2014."},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Yang X. One methodology for spam review detection based on review coherence metrics. In: Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things. IEEE, 2015; pp. 99\u2013102.","DOI":"10.1109\/ICAIOT.2015.7111547"},{"issue":"3","key":"1_CR24","doi-asserted-by":"publisher","first-page":"467","DOI":"10.13053\/cys-18-3-2035","volume":"18","author":"H Li","year":"2014","unstructured":"Li H, Liu B, Mukherjee A, Shao J. Spotting fake reviews using positive-unlabeled learning. Computaci\u00f3n y Sistemas. 2014;18(3):467\u201375.","journal-title":"Computaci\u00f3n y Sistemas"},{"key":"1_CR25","unstructured":"You Z, Qian T, Liu B. An attribute enhanced domain adaptive model for cold-start spam review detection. In: Proceedings of the 27th International Conference on Computational Linguistics, 2018; pp. 1884\u201395."},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Li Q, Wu Q, Zhu C, Zhang J, Zhao W. An inferable representation learning for fraud review detection with cold-start problem. In: 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019; pp. 1\u20138.","DOI":"10.1109\/IJCNN.2019.8852437"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Xie S, Wang G, Lin S, and Yu PS. Review spam detection via temporal pattern discovery. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012, pp. 823\u201331.","DOI":"10.1145\/2339530.2339662"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Wang G, Xie S, Liu B, Philip SY. Review graph based online store review spammer detection. In: IEEE 11th international conference on data mining. IEEE. 2011;2011:1242\u20137.","DOI":"10.1109\/ICDM.2011.124"},{"issue":"4","key":"1_CR29","first-page":"1","volume":"3","author":"G Wang","year":"2012","unstructured":"Wang G, Xie S, Liu B, Yu PS. Identify online store review spammers via social review graph. ACM Trans Intell Syst Technol (TIST). 2012;3(4):1\u201321.","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"1_CR30","doi-asserted-by":"publisher","first-page":"53801","DOI":"10.1109\/ACCESS.2020.2979226","volume":"8","author":"N Hussain","year":"2020","unstructured":"Hussain N, Mirza HT, Hussain I, Iqbal F, Memon I. Spam review detection using the linguistic and spammer behavioral methods. IEEE Access. 2020;8:53801\u201316.","journal-title":"IEEE Access"},{"key":"1_CR31","doi-asserted-by":"crossref","unstructured":"Aghakhani H, Machiry A, Nilizadeh S, Kruegel C, Vigna G. Detecting deceptive reviews using generative adversarial networks. In: IEEE Security and Privacy Workshops (SPW). IEEE. 2018;2018:89\u201395.","DOI":"10.1109\/SPW.2018.00022"},{"key":"1_CR32","doi-asserted-by":"crossref","unstructured":"Zheng P, Yuan S, Wu X, Li J, and Lu A. One-class adversarial nets for fraud detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, 2019, pp. 1286\u201393.","DOI":"10.1609\/aaai.v33i01.33011286"},{"issue":"3","key":"1_CR33","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1002\/asi.10382","volume":"55","author":"C Chen","year":"2004","unstructured":"Chen C. Mining the web: discovering knowledge from hypertext data. J Am Soc Inf Sci. 2004;55(3):275.","journal-title":"J Am Soc Inf Sci"},{"key":"1_CR34","unstructured":"Mukherjee A, Venkataraman V, Liu B, Glance N, et al. Fake review detection: classification and analysis of real and pseudo reviews. UIC-CS-03-2013. Technical Report, 2013."},{"key":"1_CR35","doi-asserted-by":"crossref","unstructured":"Alom Z, Carminati B, and Ferrari E. Detecting spam accounts on twitter. In: 2018 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2018, pp. 1191\u20138.","DOI":"10.1109\/ASONAM.2018.8508495"},{"key":"1_CR36","doi-asserted-by":"crossref","unstructured":"Swe MM and Myo NN. Fake accounts detection on twitter using blacklist. In: 2018 IEEE\/ACIS 17th International Conference on Computer and Information Science (ICIS). IEEE, 2018, pp. 562\u20136.","DOI":"10.1109\/ICIS.2018.8466499"},{"key":"1_CR37","doi-asserted-by":"crossref","unstructured":"Jia S, Zhang X, Wang X, and Liu Y. Fake reviews detection based on lda. In: 2018 4th International Conference on Information Management (ICIM). IEEE, 2018, pp. 280\u20133.","DOI":"10.1109\/INFOMAN.2018.8392850"},{"key":"1_CR38","doi-asserted-by":"crossref","unstructured":"Aritsugi M, et al. Exploiting function words feature in classifying deceptive and truthful reviews. In: 2018 Thirteenth International Conference on Digital Information Management (ICDIM). IEEE, 2018, pp. 51\u20136.","DOI":"10.1109\/ICDIM.2018.8846971"},{"key":"1_CR39","unstructured":"Mesnil G, Mikolov T, Ranzato M, and Bengio Y. Ensemble of generative and discriminative techniques for sentiment analysis of movie reviews. arXiv preprint; 2014. arXiv:1412.5335."},{"key":"1_CR40","doi-asserted-by":"crossref","unstructured":"Yang X and Yu X. Recognizing deceptive reviews based on weighted multi-instance unbalanced support vector machine. In: Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science, 2019, pp. 705\u20138.","DOI":"10.1145\/3349341.3349494"},{"key":"1_CR41","doi-asserted-by":"crossref","unstructured":"Kennedy S, Walsh N, Sloka K, Mccarren A, and Foster J. Fact or factitious? Contextualized opinion spam detection. In: Proceedings of the 57th Annual Meeting of the association for computational linguistics: student research workshop, 2019.","DOI":"10.18653\/v1\/P19-2048"},{"key":"1_CR42","unstructured":"Devlin J, Chang MW, Lee K, and Toutanova K. Bert: pre-training of deep bidirectional transformers for language understanding. 2018."},{"key":"1_CR43","doi-asserted-by":"crossref","unstructured":"Nilizadeh S, Aghakhani H, Gustafson E, Kruegel C, and Vigna G. Think outside the dataset: Finding fraudulent reviews using cross-dataset analysis. In: The World Wide Web Conference, 2019, pp. 3108\u201315.","DOI":"10.1145\/3308558.3313647"},{"key":"1_CR44","doi-asserted-by":"crossref","unstructured":"Tingxuan S and Lau RYK. Collective classification for social opinion spam detection. In: Proceedings of the 2019 2nd international conference on data science and information technology, 2019, pp. 181\u20136.","DOI":"10.1145\/3352411.3352440"},{"key":"1_CR45","doi-asserted-by":"crossref","unstructured":"Sihombing A and Fong ACM. Fake review detection on yelp dataset using classification techniques in machine learning. In: 2019 International conference on contemporary computing and informatics (IC3I). IEEE, 2019, pp. 64\u20138.","DOI":"10.1109\/IC3I46837.2019.9055644"},{"key":"1_CR46","unstructured":"Ott M, Choi Y, Cardie C, and Hancock JT. Finding deceptive opinion spam by any stretch of the imagination. arXiv preprint; 2011. arXiv:1107.4557."},{"key":"1_CR47","doi-asserted-by":"crossref","unstructured":"Barushka A and Hajek P. The effect of text preprocessing strategies on detecting fake consumer reviews. In: Proceedings of the 2019 3rd international conference on e-business and internet, 2019, pp. 13\u20137.","DOI":"10.1145\/3383902.3383908"},{"key":"1_CR48","doi-asserted-by":"crossref","unstructured":"Hassan R and Islam MR. Detection of fake online reviews using semi-supervised and supervised learning. In: 2019 International conference on electrical, computer and communication engineering (ECCE). IEEE, 2019, pp. 1\u20135.","DOI":"10.1109\/ECACE.2019.8679186"},{"key":"1_CR49","doi-asserted-by":"crossref","unstructured":"Prakash P, Shashank N, Arjun M, Yadav PS, Shreyamsa S, and Prazwal N. Fake review prevention using classification and authentication techniques. In: ICT Systems and Sustainability. Springer, 2020, pp. 397\u2013406.","DOI":"10.1007\/978-981-15-0936-0_42"},{"key":"1_CR50","doi-asserted-by":"crossref","unstructured":"Caruana R and Niculescu-Mizil A. An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd international conference on machine learning, 2006, pp. 161\u20138.","DOI":"10.1145\/1143844.1143865"},{"issue":"1","key":"1_CR51","first-page":"192","volume":"167","author":"T Hastie","year":"2004","unstructured":"Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. 2001. J Roy Stat Soc. 2004;167(1):192\u2013192.","journal-title":"J Roy Stat Soc"},{"key":"1_CR52","first-page":"899","volume":"2014","author":"H Li","year":"2014","unstructured":"Li H, Chen Z, Liu B, Wei X, Shao J. Spotting fake reviews via collective positive-unlabeled learning. IEEE Int Conf Data Min. 2014;2014:899\u2013904.","journal-title":"IEEE Int Conf Data Min."},{"key":"1_CR53","doi-asserted-by":"crossref","unstructured":"Ren Y, Ji D, and Zhang H. Positive unlabeled learning for deceptive reviews detection. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 2014, pp. 488\u201398.","DOI":"10.3115\/v1\/D14-1055"},{"key":"1_CR54","doi-asserted-by":"crossref","unstructured":"Hai Z, Zhao P, Cheng P, Yang P, Li X-L, and Li G. Deceptive review spam detection via exploiting task relatedness and unlabeled data. In: Proceedings of the 2016 conference on empirical methods in natural language processing, 2016, pp. 1817\u201326.","DOI":"10.18653\/v1\/D16-1187"},{"issue":"4","key":"1_CR55","doi-asserted-by":"publisher","first-page":"1595","DOI":"10.1109\/TCYB.2018.2877161","volume":"50","author":"Z Wu","year":"2018","unstructured":"Wu Z, Cao J, Wang Y, Wang Y, Zhang L, Wu J. hpsd: a hybrid pu-learning-based spammer detection model for product reviews. IEEE Trans Cybernet. 2018;50(4):1595\u2013606.","journal-title":"IEEE Trans Cybernet"},{"key":"1_CR56","doi-asserted-by":"crossref","unstructured":"Yilmaz CM and Durahim AO. Spr2ep: a semi-supervised spam review detection framework. In: 2018 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2018, pp. 306\u201313.","DOI":"10.1109\/ASONAM.2018.8508314"},{"issue":"3","key":"1_CR57","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1007\/s00607-019-00763-y","volume":"102","author":"W Liu","year":"2020","unstructured":"Liu W, Jing W, Li Y. Incorporating feature representation into bilstm for deceptive review detection. Computing. 2020;102(3):701\u201315.","journal-title":"Computing"},{"key":"1_CR58","doi-asserted-by":"crossref","unstructured":"Barushka A and Hajek P. Review spam detection using word embeddings and deep neural networks. In: IFIP International conference on artificial intelligence applications and innovations. Springer, 2019, pp. 340\u201350.","DOI":"10.1007\/978-3-030-19823-7_28"},{"key":"1_CR59","doi-asserted-by":"crossref","unstructured":"Archchitha K and Charles E. Opinion spam detection in online reviews using neural networks. In: 2019 19th International Conference on Advances in ICT for Emerging Regions (ICTer), vol. 250. IEEE, 2019, pp. 1\u20136.","DOI":"10.1109\/ICTer48817.2019.9023695"},{"key":"1_CR60","doi-asserted-by":"crossref","unstructured":"Yuan C, Zhou W, Ma Q, Lv S, Han J, and Hu S. Learning review representations from user and product level information for spam detection. In: 2019 IEEE international conference on data mining (ICDM). IEEE, 2019; pp. 1444\u20139.","DOI":"10.1109\/ICDM.2019.00188"},{"key":"1_CR61","doi-asserted-by":"crossref","unstructured":"Wang Z, Zhang J, Feng J, Chen Z. Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI conference on artificial intelligence, 2014; vol. 28, no. 1","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"1_CR62","doi-asserted-by":"crossref","unstructured":"Nayak A, Chen H, Ruan X, and Ouyang J. Deepspot: understanding online opinion spam by text augmentation using sentiment encoder-decoder networks. In: Proceedings of the 3rd ACM SIGSPATIAL international workshop on analytics for local events and news, 2019, pp. 1\u201310.","DOI":"10.1145\/3356473.3365187"},{"key":"1_CR63","unstructured":"Ren Y, Zhang Y. Deceptive opinion spam detection using neural network. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers, 2016; pp. 140\u201350."},{"key":"1_CR64","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M. Graph neural networks: a review of methods and applications. AI Open. 2020;1:57\u201381.","journal-title":"AI Open"},{"key":"1_CR65","doi-asserted-by":"crossref","unstructured":"Kindermann R. Markov random fields and their applications. Am Math Soc. 1980.","DOI":"10.1090\/conm\/001"},{"key":"1_CR66","doi-asserted-by":"crossref","unstructured":"Sun H, Morales A, Yan X. Synthetic review spamming and defense. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013; pp. 1088\u201396.","DOI":"10.1145\/2487575.2487688"},{"key":"1_CR67","doi-asserted-by":"crossref","unstructured":"Weng H, Li Z, Ji S, Chu C, Lu H, Du T, He Q. Online e-commerce fraud: a large-scale detection and analysis. In: 2018 IEEE 34th international conference on data engineering (ICDE). IEEE, 2018; pp. 1435\u201340.","DOI":"10.1109\/ICDE.2018.00162"},{"issue":"3","key":"1_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3305258","volume":"11","author":"H Xue","year":"2019","unstructured":"Xue H, Wang Q, Luo B, Seo H, Li F. Content-aware trust propagation toward online review spam detection. J Data Inf Quality (JDIQ). 2019;11(3):1\u201331.","journal-title":"J Data Inf Quality (JDIQ)"},{"key":"1_CR69","doi-asserted-by":"crossref","unstructured":"Yuan D, Miao Y, Gong NZ, Yang Z, Li Q, Song D, Wang Q, and Liang X. Detecting fake accounts in online social networks at the time of registrations. In: Proceedings of the 2019 ACM SIGSAC conference on computer and communications security, 2019, pp. 1423\u201338.","DOI":"10.1145\/3319535.3363198"},{"key":"1_CR70","doi-asserted-by":"crossref","unstructured":"Wang D, Lin J, Cui P, Jia Q, Wang Z, Fang Y, Yu Q, Zhou J, Yang S, and Qi Y. A semi-supervised graph attentive network for financial fraud detection. In: 2019 IEEE international conference on data mining (ICDM). IEEE, 2019, pp. 598\u2013607.","DOI":"10.1109\/ICDM.2019.00070"},{"key":"1_CR71","doi-asserted-by":"crossref","unstructured":"Liu Z, Chen C, Yang X, Zhou J, Li X, and Song L. Heterogeneous graph neural networks for malicious account detection. In: Proceedings of the 27th ACM international conference on information and knowledge management, 2018, pp. 2077\u201385.","DOI":"10.1145\/3269206.3272010"},{"key":"1_CR72","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, and Skiena S. Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014, pp. 701\u201310.","DOI":"10.1145\/2623330.2623732"},{"key":"1_CR73","unstructured":"Mikolov T, Chen K, Corrado G, and Dean J. Efficient estimation of word representations in vector space. arXiv preprint; 2013. arXiv:1301.3781."},{"key":"1_CR74","doi-asserted-by":"crossref","unstructured":"Ali\u00a0Alhosseini S, Bin\u00a0Tareaf R, Najafi P, and Meinel C. Detect me if you can: Spam bot detection using inductive representation learning. In: Companion proceedings of The 2019 World Wide Web conference, 2019, pp. 148\u201353.","DOI":"10.1145\/3308560.3316504"},{"key":"1_CR75","unstructured":"Hamilton WL, Ying R, and Leskovec J. Inductive representation learning on large graphs. arXiv preprint; 2017. arXiv:1706.02216."},{"key":"1_CR76","unstructured":"Pearl J. Probabilistic reasoning in intelligent systems: networks of plausible inference. Elsevier, 2014."},{"key":"1_CR77","doi-asserted-by":"crossref","unstructured":"Wang J, Wen R, Wu C, Huang Y, Xion J. Fdgars: fraudster detection via graph convolutional networks in online app review system. In: Companion proceedings of The 2019 World Wide Web conference, 2019; pp. 310\u20136.","DOI":"10.1145\/3308560.3316586"},{"key":"1_CR78","doi-asserted-by":"crossref","unstructured":"Ghadery E, Movahedi S, Faili H, Shakery A. Mncn: a multilingual ngram-based convolutional network for aspect category detection in online reviews. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, 2019; pp. 6441\u20138.","DOI":"10.1609\/aaai.v33i01.33016441"},{"key":"1_CR79","doi-asserted-by":"crossref","unstructured":"Dong W, Moses C, Li K. Efficient k-nearest neighbor graph construction for generic similarity measures. In: Proceedings of the 20th international conference on World wide web, 2011; pp. 577\u201386.","DOI":"10.1145\/1963405.1963487"},{"key":"1_CR80","unstructured":"Rakhlin A. \u201cConvolutional neural networks for sentence classification,\u201d GitHub, 2016."},{"key":"1_CR81","unstructured":"Ott M, Cardie C, Hancock JT. Negative deceptive opinion spam. In: Proceedings of the 2013 conference of the north American chapter of the association for computational linguistics: human language technologies, 2013; pp. 497\u2013501."},{"key":"1_CR82","doi-asserted-by":"crossref","unstructured":"He R, McAuley J. Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th international conference on world wide web, 2016; pp. 507\u201317.","DOI":"10.1145\/2872427.2883037"},{"key":"1_CR83","doi-asserted-by":"crossref","unstructured":"McAuley J, Targett C, Shi Q, Van Den Hengel A. Image-based recommendations on styles and substitutes. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, 2015; pp. 43\u201352.","DOI":"10.1145\/2766462.2767755"},{"key":"1_CR84","doi-asserted-by":"crossref","unstructured":"Jindal N, Liu B. Opinion spam and analysis. In: WSDM\u201908 - Proceedings of the 2008 international conference on web search and data mining, no. November,2008; pp. 219\u201329.","DOI":"10.1145\/1341531.1341560"},{"key":"1_CR85","unstructured":"Learning to identify review spam. IJCAI international joint conference on artificial intelligence, no. January 2011,2011; pp. 2488\u201393"},{"key":"1_CR86","doi-asserted-by":"crossref","unstructured":"Mukherjee A, Venkataraman V, Liu B, Glance N. What yelp fake review filter might be doing?. In: Proceedings of the international AAAI conference on web and social media, 2013; vol. 7, no. 1.","DOI":"10.1609\/icwsm.v7i1.14389"}],"container-title":["Human-Centric Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44230-022-00001-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44230-022-00001-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44230-022-00001-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T22:06:34Z","timestamp":1727129194000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44230-022-00001-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,5]]},"references-count":86,"journal-issue":{"issue":"1-2","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["1"],"URL":"https:\/\/doi.org\/10.1007\/s44230-022-00001-3","relation":{},"ISSN":["2667-1336"],"issn-type":[{"value":"2667-1336","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,5]]},"assertion":[{"value":"22 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}