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Truth Discovery for False Claims Detection: A Systematic Literature Review [Descubrimiento de la verdad para la detección de reclamaciones fraudulentas o falsas afirmaciones: Una revisión sistemática de literatura]

dc.contributor.authorHenao M.V
dc.contributor.authorMarín L.M.G
dc.contributor.authorVillegas H.H.J.
dc.date.accessioned2022-09-14T14:34:17Z
dc.date.available2022-09-14T14:34:17Z
dc.date.created2022
dc.identifier.issn16469895
dc.identifier.urihttp://hdl.handle.net/11407/7609
dc.descriptionThe Petitions, Complaints, Claims and Suggestions (PCCS) systems are mechanisms that clients or users use to publicize the perception and/or conformity with respect to a good or service. However, when a person or entity makes false claims and therefore misleading claims to obtain compensation or benefits to which they are not entitled, it is known as fraud. This work aims to review and investigate the documentary status of investigations carried out with reference to the subject. The analysis methodology is based on the collection of scientific information that has been published in the period 2015-2020. A search equation is proposed, 5 research questions and in a process of several phases, 17 relevant documents were selected. © 2022, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.eng
dc.language.isospa
dc.publisherAssociacao Iberica de Sistemas e Tecnologias de Informacao
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85127591515&partnerID=40&md5=362afa0d2238cce8e37d3a0926b4a262
dc.sourceRISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
dc.titleTruth Discovery for False Claims Detection: A Systematic Literature Review [Descubrimiento de la verdad para la detección de reclamaciones fraudulentas o falsas afirmaciones: Una revisión sistemática de literatura]
dc.typeArticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Sistemas
dc.publisher.programCiencias Básicas
dc.type.spaArtículo
dc.subject.keywordAbusive claimseng
dc.subject.keywordClaimseng
dc.subject.keywordFalse claimseng
dc.subject.keywordFraud detectioneng
dc.subject.keywordTruth discoveryeng
dc.relation.citationissueE47
dc.relation.citationstartpage377
dc.relation.citationendpage389
dc.publisher.facultyFacultad de Ingenierías
dc.publisher.facultyFacultad de Ciencias Básicas
dc.affiliationHenao, M.V., Modelación y Ciencia Computacional, Universidad de Medellín, Antioquia, Colombia
dc.affiliationMarín, L.M.G., Facultad de Ingenierías, Universidad de Medellín, Antioquia, Colombia
dc.affiliationVillegas, H.H.J., Facultad de Ciencias Básicas, Universidad de Medellín, Antioquia, Colombia
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dc.relation.referencesBauder, R. A., Khoshgoftaar, T. M., Hasanin, T., Data sampling approaches with severely imbalanced big data for medicare fraud detection (2018) Proceedings-International Conference on Tools with Artificial Intelligence, ICTAI, pp. 137-142. , https://doi.org/10.1109/ICTAI.2018.00030, 2018-Novem
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dc.relation.referencesHuang, C., Wang, D., Spatial-Temporal Aware Truth Finding in Big Data Social Sensing Applications (2015) Proceedings-14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015, 2, pp. 72-79. , https://doi.org/10.1109/Trustcom.2015.564
dc.relation.referencesHuang, C., Wang, D., Topic-Aware Social Sensing with Arbitrary Source Dependency Graphs (2016) 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016-Proceedings, , https://doi.org/10.1109/IPSN.2016.7460724
dc.relation.referencesHuang, C., Wang, D., Chawla, N., Towards time-sensitive truth discovery in social sensing applications (2015) Proceedings-2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2015, pp. 154-162. , https://doi.org/10.1109/MASS.2015.39
dc.relation.referencesKareem, S., Ahmad, R. B., Sarlan, A. B., Framework for the identification of fraudulent health insurance claims using association rule mining (2018) 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017, pp. 99-104. , https://doi.org/10.1109/ICBDAA.2017.8284114, 2018-Janua
dc.relation.referencesMarshall, J., Syed, M., Wang, D., Hardness-aware truth discovery in social sensing applications (2016) Proceedings-12th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2016, pp. 143-152. , https://doi.org/10.1109/DCOSS.2016.9
dc.relation.referencesMarshall, J., Wang, D., Towards Emotional-Aware Truth Discovery in Social Sensing Applications (2016) 2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016, , https://doi.org/10.1109/SMARTCOMP.2016.7501723
dc.relation.referencesPérez, J., (2012) Revisión Sistemática de Literatura en Ingeniería, , (Sello Editorial Universidad de Antioquia, dic. 2012, ISBN: 978-958-714-543-4)
dc.relation.referencesPérez, J., (2019) Revisión sistemática de literatura en Ingeniería Ampliada y actualizada, , Segunda Edición, April, 0–183
dc.relation.referencesRawte, V., Anuradha, G., Fraud detection in health insurance using data mining techniques (2015) Proceedings-2015 International Conference on Communication, Information and Computing Technology, ICCICT 2015, pp. 14-18. , https://doi.org/10.1109/ICCICT.2015.7045689
dc.relation.referencesRoy, R., George, K. T., Detecting insurance claims fraud using machine learning techniques (2017) Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2017, , https://doi.org/10.1109/ICCPCT.2017.8074258
dc.relation.referencesShiralkar, P., Flammini, A., Menczer, F., Ciampaglia, G. L., Finding streams in knowledge graphs to support fact checking (2017) Proceedings-IEEE International Conference on Data Mining, ICDM, pp. 859-864. , https://doi.org/10.1109/ICDM.2017.105, 2017-Novem
dc.relation.referencesVerma, A., Taneja, A., Arora, A., Fraud detection and frequent pattern matching in insurance claims using data mining techniques (2018) 2017 10th International Conference on Contemporary Computing, IC3 2017, pp. 1-7. , https://doi.org/10.1109/IC3.2017.8284299, 2018-Janua(August)
dc.relation.referencesZhang, D., Wang, D., Vance, N., Zhang, Y., Mike, S., On Scalable and Robust Truth Discovery in Big Data Social Media Sensing Applications (2018) IEEE Transactions on Big Data, 5 (2), pp. 195-208. , https://doi.org/10.1109/tbdata.2018.2824812
dc.type.coarhttp://purl.org/coar/resource_type/c_6501
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/article
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


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