RT Journal Article
T1 On the feasibility of predicting volumes of fake news- the Spanish case
A1 Ibáñez Lissen, Luis
A1 González Manzano, Lorena
A1 Fuentes García-Romero de Tejada, José María de
A1 Goyanes Martínez, Manuel
AB 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.
PB IEEE
SN 2329-924X
YR 2023
FD 2023-07-13
LK https://hdl.handle.net/10016/39026
UL https://hdl.handle.net/10016/39026
LA eng
NO 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
DS e-Archivo
RD 20 may. 2024