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Predicting image credibility in fake news over social media using multi-modal approach
Social media are the main contributors to spreading fake images. Fake images are manipulated images altered through software or by other means to change the information they convey. Fake images propagated over microblogging platforms generate misrepresentation and stimulate polarization in the peopl...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8143443/ https://www.ncbi.nlm.nih.gov/pubmed/34054227 http://dx.doi.org/10.1007/s00521-021-06086-4 |
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author | Singh, Bhuvanesh Sharma, Dilip Kumar |
author_facet | Singh, Bhuvanesh Sharma, Dilip Kumar |
author_sort | Singh, Bhuvanesh |
collection | PubMed |
description | Social media are the main contributors to spreading fake images. Fake images are manipulated images altered through software or by other means to change the information they convey. Fake images propagated over microblogging platforms generate misrepresentation and stimulate polarization in the people. Detection of fake images shared over social platforms is extremely critical to mitigating its spread. Fake images are often associated with textual data. Hence, a multi-modal framework is employed utilizing visual and textual feature learning. However, few multi-modal frameworks are already proposed; they are further dependent on additional tasks to learn the correlation between modalities. In this paper, an efficient multi-modal approach is proposed, which detects fake images of microblogging platforms. No further additional subcomponents are required. The proposed framework utilizes explicit convolution neural network model EfficientNetB0 for images and sentence transformer for text analysis. The feature embedding from visual and text is passed through dense layers and later fused to predict fake images. To validate the effectiveness, the proposed model is tested upon a publicly available microblogging dataset, MediaEval (Twitter) and Weibo, where the accuracy prediction of 85.3% and 81.2% is observed, respectively. The model is also verified against the newly created latest Twitter dataset containing images based on India's significant events in 2020. The experimental results illustrate that the proposed model performs better than other state-of-art multi-modal frameworks. |
format | Online Article Text |
id | pubmed-8143443 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-81434432021-05-25 Predicting image credibility in fake news over social media using multi-modal approach Singh, Bhuvanesh Sharma, Dilip Kumar Neural Comput Appl S.i. : Ncacvip Social media are the main contributors to spreading fake images. Fake images are manipulated images altered through software or by other means to change the information they convey. Fake images propagated over microblogging platforms generate misrepresentation and stimulate polarization in the people. Detection of fake images shared over social platforms is extremely critical to mitigating its spread. Fake images are often associated with textual data. Hence, a multi-modal framework is employed utilizing visual and textual feature learning. However, few multi-modal frameworks are already proposed; they are further dependent on additional tasks to learn the correlation between modalities. In this paper, an efficient multi-modal approach is proposed, which detects fake images of microblogging platforms. No further additional subcomponents are required. The proposed framework utilizes explicit convolution neural network model EfficientNetB0 for images and sentence transformer for text analysis. The feature embedding from visual and text is passed through dense layers and later fused to predict fake images. To validate the effectiveness, the proposed model is tested upon a publicly available microblogging dataset, MediaEval (Twitter) and Weibo, where the accuracy prediction of 85.3% and 81.2% is observed, respectively. The model is also verified against the newly created latest Twitter dataset containing images based on India's significant events in 2020. The experimental results illustrate that the proposed model performs better than other state-of-art multi-modal frameworks. Springer London 2021-05-24 2022 /pmc/articles/PMC8143443/ /pubmed/34054227 http://dx.doi.org/10.1007/s00521-021-06086-4 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | S.i. : Ncacvip Singh, Bhuvanesh Sharma, Dilip Kumar Predicting image credibility in fake news over social media using multi-modal approach |
title | Predicting image credibility in fake news over social media using multi-modal approach |
title_full | Predicting image credibility in fake news over social media using multi-modal approach |
title_fullStr | Predicting image credibility in fake news over social media using multi-modal approach |
title_full_unstemmed | Predicting image credibility in fake news over social media using multi-modal approach |
title_short | Predicting image credibility in fake news over social media using multi-modal approach |
title_sort | predicting image credibility in fake news over social media using multi-modal approach |
topic | S.i. : Ncacvip |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8143443/ https://www.ncbi.nlm.nih.gov/pubmed/34054227 http://dx.doi.org/10.1007/s00521-021-06086-4 |
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