On the feasibility of predicting volumes of fake news- the Spanish case

Ibáñez Lissen, Luis; González Manzano, Lorena; Fuentes García-Romero de Tejada, José María de; Goyanes Martínez, Manuel

Publication:
On the feasibility of predicting volumes of fake news- the Spanish case

dc.affiliation.dpto	UC3M. Departamento de Comunicación	es
dc.affiliation.dpto	UC3M. Departamento de Informática	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: COSEC (Computer SECurity Lab)	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Innovation on Digital Media	es
dc.contributor.author	Ibáñez Lissen, Luis
dc.contributor.author	González Manzano, Lorena
dc.contributor.author	Fuentes García-Romero de Tejada, José María de
dc.contributor.author	Goyanes Martínez, Manuel
dc.contributor.funder	Comunidad de Madrid	es
dc.contributor.funder	European Commission	en
dc.contributor.funder	Ministerio de Ciencia e Innovación (España)	es
dc.contributor.funder	Universidad Carlos III de Madrid	es
dc.date.accessioned	2023-12-04T08:32:36Z
dc.date.available	2023-12-04T08:32:36Z
dc.date.issued	2023-07-13
dc.description.abstract	The growing amount of news shared on the Internet makes it hard to verify them in real-time. Malicious actors take advantage of this situation by spreading fake news to impact society through misinformation. An estimation of future fake news would help to focus the detection and verification efforts. Unfortunately, no previous work has addressed this issue yet. Therefore, this work measures the feasibility of predicting the volume of future fake news in a particular context—Spanish contents related to Spain. The approach involves different artificial intelligence (AI) mechanisms on a dataset of 298k real news and 8.9k fake news in the period 2019–2022. Results show that very accurate predictions can be reached. In general words, the use of long short-term memory (LSTM) with attention mechanisms offers the best performance, being headlines useful when a small amount of days is taken as input. In the best cases, when predictions are made for periods, an error of 10.3% is made considering the mean of fake news. This error raises to 28.7% when predicting a single day in the future.	en
dc.description.sponsorship	This work was supported in part by the Universidad Carlos III de Madrid (UC3M) and the Government of Madrid [Community of Madrid (CAM)] under Grant DEPROFAKE-CM-UC3M; in part by the CAM through the Project CYNAMON, co-funded by the European Research Development Fund (ERDF), under Grant P2018/TCS-4566-CM; and in part by the Spanish Ministry of Science and Innovation (MICINN) of Spain under Grant PID2019-111429RB-C21	en
dc.identifier.bibliographicCitation	L. Ibañez-Lissen, L. González-Manzano, J. M. de Fuentes and M. Goyanes, "On the Feasibility of Predicting Volumes of Fake News—The Spanish Case," in IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2023.3297093.	en
dc.identifier.doi	https://doi.org/10.1109/TCSS.2023.3297093
dc.identifier.issn	2329-924X
dc.identifier.publicationfirstpage	1
dc.identifier.publicationlastpage	11
dc.identifier.publicationtitle	IEEE Transactions on Computational Social Systems	en
dc.identifier.uri	https://hdl.handle.net/10016/39026
dc.identifier.uxxi	AR/0000033290
dc.language.iso	eng
dc.publisher	IEEE
dc.relation.projectID	Comunidad de Madrid. S2018/TCS-4566	es
dc.relation.projectID	Gobierno de España. PID2019-111429RB-C21	es
dc.relation.projectID	Comunidad de Madrid. DEPROFAKE-CM-UC3M	es
dc.rights	© The Authors, 2023	en
dc.rights	Atribución 3.0 España
dc.rights.accessRights	open access	en
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/es/
dc.subject.eciencia	Informática	es
dc.subject.other	fake news	en
dc.subject.other	machine learning	en
dc.subject.other	prediction	en
dc.title	On the feasibility of predicting volumes of fake news- the Spanish case	en
dc.type	research article	*
dc.type.hasVersion	VoR	*
dspace.entity.type	Publication