RT Journal Article
T1 Multimodal Fake News Detection
A1 Segura-Bedmar, Isabel
A1 Alonso Bartolomé, Santiago
AB Over the last few years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have on different segments of our society. Thus, the development of tools for the automatic detection of fake news plays an important role in the prevention of its negative effects. Most attempts to detect and classify false content focus only on using textual information. Multimodal approaches are less frequent and they typically classify news either as true or fake. In this work, we perform a fine-grained classification of fake news on the Fakeddit dataset, using both unimodal and multimodal approaches. Our experiments show that the multimodal approach based on a Convolutional Neural Network (CNN) architecture combining text and image data achieves the best results, with an accuracy of 87%. Some fake news categories, such as Manipulated content, Satire, or False connection, strongly benefit from the use of images. Using images also improves the results of the other categories but with less impact. Regarding the unimodal approaches using only text, Bidirectional Encoder Representations from Transformers (BERT) is the best model, with an accuracy of 78%. Exploiting both text and image data significantly improves the performance of fake news detection.
PB MDPI
SN 2078-2489
YR 2022
FD 2022-06-02
LK https://hdl.handle.net/10016/36984
UL https://hdl.handle.net/10016/36984
LA eng
NO This research was funded by the Madrid Government (Comunidad de Madrid) under theMultiannual Agreement with UC3M in the line of “Fostering Young Doctors Research” (NLP4RARECM-UC3M) and in the context of the V PRICIT (Regional Programme of Research and TechnologicalInnovation) and under the Multiannual Agreement with UC3M in the line of Excellence of UniversityProfessors (EPUC3M17).
DS e-Archivo
RD 1 sept. 2024