SpotFake: A Multi-modal Framework for Fake News Detection

Singhal, Shivangi; Shah, Rajiv Ratn; Chakraborty, Tanmoy; Kumaraguru, Ponnurangam; Satoh, Shin’ichi

doi:10.1109/bigmm.2019.00-44

Cited by 267 publications

(143 citation statements)

References 18 publications

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“…The research in [13] proposes a Tri-relationship embedding TriFN system, which simultaneously models publisher-news relationships and user-news interactions to identify fake news. The SpotFake system in [14] is a multimodal framework for fake news detection. This method detects fake news without taking any subtasks into account.…”

Section: Related Workmentioning

confidence: 99%

Towards Evaluating the COVID’19 related Fake News Problem: Case of Morocco

Maakoul

Boucht

Hachimi

et al. 2020

2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS)

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Nowadays social media play an important role in our societies. In fact, they remain one of the most commonly used means to easily obtain information online. However, social media can also be a catalyst for the proliferation of fake news around the world, especially in times of crisis where massive misinformation can have serious consequences. In particular, fake news about the COVID'19, as the virus spreads, leads to an infodemic of misinformation, hence the need for news verification mechanisms in social media. In this paper, we approach the problem of fake news related to COVID'19 as a global pandemic with a significant impact on various sectors. We also provide an aggregation system to detect and analyze fake news related to the COVID'19 pandemic in the Moroccan context based on data sets scrapped from Facebook.

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Section: Related Workmentioning

confidence: 99%

Towards Evaluating the COVID’19 related Fake News Problem: Case of Morocco

Maakoul

Boucht

Hachimi

et al. 2020

2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS)

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“…Later, it considers both these probabilities along with the calculated similarity index between the text and visual content to classify it as fake or not. Another prominent multi-modal framework proposed by other researchers is Spotfake [42].…”

Section: Multi-modal Methodsmentioning

confidence: 99%

Predicting image credibility in fake news over social media using multi-modal approach

Singh

Sharma

2021

Neural Comput & Applic

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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.

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“…Towards this direction, experiments were conducted by extracting features from two different modalities such as text and image. Works on that direction include EANN [37], MVAE [17], SpotFake [36] and SpotFake+ [35]. The EANN model, short for event adversarial neural networks, for multimodal fake news detection proposed by [37] consists of three sub-modules namely, textual feature extractor, visual feature extractor and an event discriminator module that when combined together is successful in detecting fake news.…”

Section: Related Workmentioning

confidence: 99%

“…This architecture too consists of three sub modules. Later methods for the task include SpotFake [36] and SpotFake+ [35]. SpotFake extracted features from both the text and image modality.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Fake News Detection

Gangireddy

Deepak

Long

et al. 2020

Proceedings of the 31st ACM Conference on Hypertext and Social Media

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Fake news has become more prevalent than ever, correlating with the rise of social media that allows every user to rapidly publish their views or hearsay. Today, fake news spans almost every realm of human activity, across diverse fields such as politics and healthcare. Most existing methods for fake news detection leverage supervised learning methods and expect a large labelled corpus of articles and social media user engagement information, which are often hard, time-consuming and costly to procure. In this paper, we consider the task of unsupervised fake news detection, which considers fake news detection in the absence of labelled historical data. We develop GTUT, a graph-based approach for the task which operates in three phases. Starting off with identifying a seed set of fake and legitimate articles exploiting high-level observations on inter-user behavior in fake news propagation, it progressively expands the labelling to all articles in the dataset. Our technique draws upon graph-based methods such as biclique identification, graph-based feature vector learning and label spreading. Through an extensive empirical evaluation over multiple real-world datasets, we establish the improved effectiveness of our method over state-of-the-art techniques for the task.

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SpotFake: A Multi-modal Framework for Fake News Detection

Cited by 267 publications

References 18 publications

Towards Evaluating the COVID’19 related Fake News Problem: Case of Morocco

Towards Evaluating the COVID’19 related Fake News Problem: Case of Morocco

Predicting image credibility in fake news over social media using multi-modal approach

Unsupervised Fake News Detection

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