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Combating multimodal fake news on social media: methods, datasets, and future perspective

verfasst von: Sakshini Hangloo, Bhavna Arora

Erschienen in: Multimedia Systems | Ausgabe 6/2022

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Abstract

The growth in the use of social media platforms such as Facebook and Twitter over the past decade has significantly facilitated and improved the way people communicate with each other. However, the information that is available and shared online is not always credible. These platforms provide a fertile ground for the rapid propagation of breaking news along with other misleading information. The enormous amounts of fake news present online have the potential to trigger serious problems at an individual level and in society at large. Detecting whether the given information is fake or not is a challenging problem and the traits of social media makes the task even more complicated as it eases the generation and spread of content to the masses leading to an enormous volume of content to analyze. The multimedia nature of fake news on online platforms has not been explored fully. This survey presents a comprehensive overview of the state-of-the-art techniques for combating fake news on online media with the prime focus on deep learning (DL) techniques keeping multimodality under consideration. Apart from this, various DL frameworks, pre-trained models, and transfer learning approaches are also underlined. As till date, there are only limited multimodal datasets that are available for this task, the paper highlights various data collection strategies that can be used along with a comparative analysis of available multimodal fake news datasets. The paper also highlights and discusses various open areas and challenges in this direction.

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Titel: Combating multimodal fake news on social media: methods, datasets, and future perspective
verfasst von: Sakshini Hangloo
Bhavna Arora
Publikationsdatum: 07.07.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Multimedia Systems / Ausgabe 6/2022
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI: https://doi.org/10.1007/s00530-022-00966-y

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