This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007%2FBF00994018.CortesC.VapnikV.1995Support-vector networksMachine Learning203273297https://doi.org/10.1007%2FBF00994018.10.1007/BF00994018Search in Google Scholar
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010.FawcettT.2006An introduction to ROC analysisPattern Recognition Letters278861874https://doi.org/10.1016/j.patrec.2005.10.010.10.1016/j.patrec.2005.10.010Search in Google Scholar
Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., & Ghosh, R. (2013). Exploiting burstiness in reviews for review spammer detection. In Proceedings of 7th International AAAI Conference on Weblogs and Social Media (ICWSM), pp. 175–184.FeiG.MukherjeeA.LiuB.HsuM.CastellanosM.GhoshR.2013Exploiting burstiness in reviews for review spammer detectionInProceedings of 7th International AAAI Conference on Weblogs and Social Media (ICWSM)17518410.1609/icwsm.v7i1.14400Search in Google Scholar
Feng, S., Banerjee, R., & Choi, Y. (2012). Syntactic stylometry for deception detection. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 171–175.FengS.BanerjeeR.ChoiY.2012Syntactic stylometry for deception detectionInProceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)171175Search in Google Scholar
Fontanarava, J., Pasi, G., & Viviani, M. (2017). Feature analysis for fake review detection through supervised classification. In Proceedings of 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 658–666. https://doi.org/10.1109/dsaa.2017.51.FontanaravaJ.PasiG.VivianiM.2017Feature analysis for fake review detection through supervised classificationInProceedings of 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA)658666666https://doi.org/10.1109/dsaa.2017.51.10.1109/DSAA.2017.51Search in Google Scholar
Heydari, A., Tavakoli, M., & Salim, N. (2016). Detection of fake opinions using time series. Expert Systems with Applications, 58, 83–92. https://doi.org/10.1016/j.eswa.2016.03.020.HeydariA.TavakoliM.SalimN.2016Detection of fake opinions using time seriesExpert Systems with Applications588392https://doi.org/10.1016/j.eswa.2016.03.020.10.1016/j.eswa.2016.03.020Search in Google Scholar
Jindal, N., & Liu, B. (2007a). Analyzing and detecting review spam. In Proceedings of 7th IEEE International Conference on Data Mining (ICDM), pp. 547–552. https://doi.org/10.1109/icdm.2007.68.JindalN.LiuB.2007aAnalyzing and detecting review spamInProceedings of 7th IEEE International Conference on Data Mining (ICDM)547552https://doi.org/10.1109/icdm.2007.68.10.1109/ICDM.2007.68Search in Google Scholar
Jindal, N., & Liu, B. (2008). Opinion spam and analysis. In Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 219–230. https://doi.org/10.1145/1341531.1341560.JindalN.LiuB.2008Opinion spam and analysisInProceedings of the 2008 International Conference on Web Search and Data Mining219230https://doi.org/10.1145/1341531.1341560.10.1145/1341531.1341560Search in Google Scholar
Jindal, N., & Liu, B. (2007b). Review spam detection. In Proceedings of the 16th International Conference on World Wide Web (WWW), pp. 1189–1190. https://doi.org/10.1145/1242572.1242759.JindalN.LiuB.2007bReview spam detectionInProceedings of the 16th International Conference on World Wide Web (WWW)11891190https://doi.org/10.1145/1242572.1242759.10.1145/1242572.1242759Search in Google Scholar
Li, F., Huang, M., Yang, Y., & Zhu, X. (2011). Learning to identify review spam. In Proceeding of the 22nd International Joint Conference on Artificial Intelligence (IJCAI’11), pp. 2488–2493.LiF.HuangM.YangY.ZhuX.2011Learning to identify review spamInProceeding of the 22nd International Joint Conference on Artificial Intelligence (IJCAI’11)24882493Search in Google Scholar
Li, J., Cardie, C., & Li, S. (2013). Topicspam: a topic-model based approach for spam detection. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 217–221.LiJ.CardieC.LiS.2013Topicspam: a topic-model based approach for spam detectionInProceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)217221Search in Google Scholar
Li, J., Ott, M., Cardie, C., & Hovy, E. (2014). Towards a general rule for identifying deceptive opinion spam. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1566–1576.LiJ.OttM.CardieC.HovyE.2014Towards a general rule for identifying deceptive opinion spamInProceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)1566157610.3115/v1/P14-1147Search in Google Scholar
Lim, E.P., Nguyen, V.A., Jindal, N., Liu, B., & Lauw, H.W. (2010). Detecting product review spammers using rating behaviors. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM), pp. 939–948. https://doi.org/10.1145/1871437.1871557.LimE.P.NguyenV.A.JindalN.LiuB.LauwH.W.2010Detecting product review spammers using rating behaviorsInProceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM)939948https://doi.org/10.1145/1871437.1871557.10.1145/1871437.1871557Search in Google Scholar
Luca, M., & Zervas, G. (2016). Fake it till you make it: Reputation, competition, and Yelp review fraud. Management Science, 62(12), 3412–3427. https://doi.org/10.1287/mnsc.2015.2304.LucaM.ZervasG.2016Fake it till you make it: Reputation, competition, and Yelp review fraudManagement Science621234123427https://doi.org/10.1287/mnsc.2015.2304.10.1287/mnsc.2015.2304Search in Google Scholar
Mitchell, T.M. (2005). Logistic Regression. Machine learning, 10: 701.MitchellT.M.2005Logistic RegressionMachine learning10701Search in Google Scholar
Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R. (2013a). Spotting opinion spammers using behavioral footprints. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 632–640. https://doi.org/10.1145/2487575.2487580.MukherjeeA.KumarA.LiuB.WangJ.HsuM.CastellanosM.GhoshR.2013aSpotting opinion spammers using behavioral footprintsInProceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining632640https://doi.org/10.1145/2487575.2487580.10.1145/2487575.2487580Search in Google Scholar
Mukherjee, A., Liu, B., & Glance, N. (2012). Spotting fake reviewer groups in consumer reviews. In Proceedings of the 21st International Conference on World Wide Web (WWW), pp. 191–200. https://doi.org/10.1145/2187836.2187863.MukherjeeA.LiuB.GlanceN.2012Spotting fake reviewer groups in consumer reviewsInProceedings of the 21st International Conference on World Wide Web (WWW)191200https://doi.org/10.1145/2187836.2187863.10.1145/2187836.2187863Search in Google Scholar
Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N. (2013b). Fake review detection: Classification and analysis of real and pseudo reviews. Technical Report UIC-CS-2013–03, University of Illinois at Chicago, Tech. Rep.MukherjeeA.VenkataramanV.LiuB.GlanceN.2013bFake review detection: Classification and analysis of real and pseudo reviewsTechnical Report UIC-CS-2013–03, University of Illinois at Chicago, Tech. Rep.Search in Google Scholar
Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N.S. (2013c). What yelp fake review filter might be doing?. In Proceedings of 7th International AAAI Conference on Weblogs and Social Media (ICWSM), pp. 409–418.MukherjeeA.VenkataramanV.LiuB.GlanceN.S.2013cWhat yelp fake review filter might be doing?InProceedings of 7th International AAAI Conference on Weblogs and Social Media (ICWSM)40941810.1609/icwsm.v7i1.14389Search in Google Scholar
Ott, M., Choi, Y., Cardie, C., & Hancock, J.T. (2011). Finding deceptive opinion spam by any stretch of the imagination. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 309–319.OttM.ChoiY.CardieC.HancockJ.T.2011Finding deceptive opinion spam by any stretch of the imaginationInProceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1309319Search in Google Scholar
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O. et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(Nov.), 2825–2830.PedregosaF.VaroquauxG.GramfortA.MichelV.ThirionB.GriselO.2011Scikit-learn: Machine learning in PythonJournal of Machine Learning Research12Nov.28252830Search in Google Scholar
Rastogi, A., & Mehrotra, M. (2018). Impact of behavioral and textual features on opinion spam detection. In Proceedings of the 2018 International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, pp. 852–857. https://doi.org/10.1109/iccons.2018.8662912.RastogiA.MehrotraM.2018Impact of behavioral and textual features on opinion spam detectionInProceedings of the 2018 International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE852857https://doi.org/10.1109/iccons.2018.8662912.10.1109/ICCONS.2018.8662912Search in Google Scholar
Rastogi, A., & Mehrotra, M. (2017). Opinion Spam Detection in Online Reviews. Journal of Information & Knowledge Management, 16(04): 1750036. https://doi.org/10.1142/s0219649217500368.RastogiA.MehrotraM.2017Opinion Spam Detection in Online ReviewsJournal of Information & Knowledge Management16041750036https://doi.org/10.1142/s0219649217500368.10.1142/S0219649217500368Search in Google Scholar
Rayana, S., & Akoglu, L. (2016). Collective opinion spam detection using active inference. In Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 630–638. https://doi.org/10.1137/1.9781611974348.71.RayanaS.AkogluL.2016Collective opinion spam detection using active inferenceInProceedings of the 2016 SIAM International Conference on Data Mining630638https://doi.org/10.1137/1.9781611974348.71.10.1137/1.9781611974348.71Search in Google Scholar
Rayana, S., & Akoglu, L. (2015). Collective opinion spam detection: Bridging review networks and metadata. In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 985–994. https://doi.org/10.1145/2783258.2783370.RayanaS.AkogluL.2015Collective opinion spam detection: Bridging review networks and metadataInProceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining985994https://doi.org/10.1145/2783258.2783370.10.1145/2783258.2783370Search in Google Scholar
Rish, I. (2001). An empirical study of the naive Bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence, 3(22), pp. 41–46.RishI.2001An empirical study of the naive Bayes classifierIn IJCAI 2001 workshop on empirical methods in artificial intelligence3224146Search in Google Scholar
Savage, D., Zhang, X., Yu, X., Chou, P., & Wang, Q. (2015). Detection of opinion spam based on anomalous rating deviation. Expert Systems with Applications, 42(22), 8650–8657. https://doi.org/10.1016/j.eswa.2015.07.019.SavageD.ZhangX.YuX.ChouP.WangQ.2015Detection of opinion spam based on anomalous rating deviationExpert Systems with Applications422286508657https://doi.org/10.1016/j.eswa.2015.07.019.10.1016/j.eswa.2015.07.019Search in Google Scholar
Schurmann, J. (1996). Pattern classification: a unified view of statistical and neural approaches. John Wiley & Sons, Inc., New York.SchurmannJ.1996Pattern classification: a unified view of statistical and neural approachesJohn Wiley & Sons, Inc.New YorkSearch in Google Scholar
Sun, C., Du, Q., & Tian, G. (2016). Exploiting product related review features for fake review detection. Mathematical Problems in Engineering, 2016(1), 1–7. https://dx.doi.org/10.1155/2016/4935792.SunC.DuQ.TianG.2016Exploiting product related review features for fake review detectionMathematical Problems in Engineering2016117https://dx.doi.org/10.1155/2016/4935792.10.1155/2016/4935792Search in Google Scholar
Wang, G., Xie, S., Liu, B., & Yu, P.S. (2012). Identify online store review spammers via social review graph. ACM Transactions on Intelligent Systems and Technology (TIST), 3(4), No. 61. https://doi.org/10.1145/2337542.2337546.WangG.XieS.LiuB.YuP.S.2012Identify online store review spammers via social review graphACM Transactions on Intelligent Systems and Technology (TIST)3461https://doi.org/10.1145/2337542.2337546.10.1145/2337542.2337546Search in Google Scholar
Xie, S., Wang, G., Lin, S., & Yu, P.S. (2012). Review spam detection via temporal pattern discovery. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 823–831. https://doi.org/10.1145/2339530.2339662.XieS.WangG.LinS.YuP.S.2012Review spam detection via temporal pattern discoveryInProceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining823831https://doi.org/10.1145/2339530.2339662.10.1145/2339530.2339662Search in Google Scholar
Yuan, Y., Xie, S., Lu, C.T., Tang, J., & Philip, S.Y. (2016). Interpretable and effective opinion spam detection via temporal patterns mining across websites. In proceedings of 2016 IEEE International Conference on Big Data. pp. 96–105. https://doi.org/10.1109/bigdata.2016.7840593.YuanY.XieS.LuC.T.TangJ.PhilipS.Y.2016Interpretable and effective opinion spam detection via temporal patterns mining across websitesIn proceedings of 2016 IEEE International Conference on Big Data96105https://doi.org/10.1109/bigdata.2016.7840593.10.1109/BigData.2016.7840593Search in Google Scholar
