Publication
Exposing and explaining fake news on-the-fly
| dc.contributor.author | de Arriba Pérez, Francisco | |
| dc.contributor.author | García Méndez, Silvia | |
| dc.contributor.author | Leal, Fátima | |
| dc.contributor.author | Malheiro, Benedita | |
| dc.contributor.author | Burguillo, Juan C. | |
| dc.date.accessioned | 2024-07-31T08:48:06Z | |
| dc.date.available | 2024-07-31T08:48:06Z | |
| dc.date.issued | 2024 | |
| dc.description | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially supported by: (i) Xunta de Galicia grants ED481B-2021-118 and ED481B-2022-093, Spain; (ii) Portuguese national funds through FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) – as part of project UIDB/50014/2020; and (iii) University of Vigo/CISUG for open access charge. | pt_PT |
| dc.description.abstract | Social media platforms enable the rapid dissemination and consumption of information. However, users instantly consume such content regardless of the reliability of the shared data. Consequently, the latter crowdsourcing model is exposed to manipulation. This work contributes with an explainable and online classification method to recognize fake news in real-time. The proposed method combines both unsupervised and supervised Machine Learning approaches with online created lexica. The profiling is built using creator-, content- and context-based features using Natural Language Processing techniques. The explainable classification mechanism displays in a dashboard the features selected for classification and the prediction confidence. The performance of the proposed solution has been validated with real data sets from Twitter and the results attain 80 % accuracy and macro F-measure. This proposal is the first to jointly provide data stream processing, profiling, classification and explainability. Ultimately, the proposed early detection, isolation and explanation of fake news contribute to increase the quality and trustworthiness of social media contents. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | de Arriba-Pérez, F., García-Méndez, S., Leal, F. et al. Exposing and explaining fake news on-the-fly. Mach Learn 113, 4615–4637 (2024). https://doi.org/10.1007/s10994-024-06527-w | pt_PT |
| dc.identifier.doi | 10.1007/s10994-024-06527-w | pt_PT |
| dc.identifier.issn | 0885-6125 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/25863 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Springer | pt_PT |
| dc.relation | INESC TEC- Institute for Systems and Computer Engineering, Technology and Science | |
| dc.relation.publisherversion | 0885-6125 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Artificial intelligence | pt_PT |
| dc.subject | Data stream architecture | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Natural language processing | pt_PT |
| dc.subject | Reliability and transparency | pt_PT |
| dc.subject | Social networking | pt_PT |
| dc.title | Exposing and explaining fake news on-the-fly | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | INESC TEC- Institute for Systems and Computer Engineering, Technology and Science | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50014%2F2020/PT | |
| oaire.citation.endPage | 4637 | pt_PT |
| oaire.citation.issue | 7 | pt_PT |
| oaire.citation.startPage | 4615 | pt_PT |
| oaire.citation.title | Machine Learning | pt_PT |
| oaire.citation.volume | 113 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | de Arriba Pérez | |
| person.familyName | García Méndez | |
| person.familyName | Leal | |
| person.familyName | BENEDITA CAMPOS NEVES MALHEIRO | |
| person.familyName | Burguillo Rial | |
| person.givenName | Francisco | |
| person.givenName | Silvia | |
| person.givenName | Fátima | |
| person.givenName | MARIA | |
| person.givenName | Juan Carlos | |
| person.identifier.ciencia-id | 2211-3EC7-B4B6 | |
| person.identifier.ciencia-id | 7A15-08FC-4430 | |
| person.identifier.orcid | 0000-0002-1140-679X | |
| person.identifier.orcid | 0000-0003-0533-1303 | |
| person.identifier.orcid | 0000-0003-4418-2590 | |
| person.identifier.orcid | 0000-0001-9083-4292 | |
| person.identifier.orcid | 0000-0001-9869-7448 | |
| person.identifier.rid | D-2450-2018 | |
| person.identifier.rid | ABF-4227-2020 | |
| person.identifier.rid | Y-3460-2019 | |
| person.identifier.rid | E-9091-2016 | |
| person.identifier.scopus-author-id | 56891654000 | |
| person.identifier.scopus-author-id | 57201127684 | |
| person.identifier.scopus-author-id | 57190765181 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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