Attributional analysis of Multi-Modal Fake News Detection Models (Grand Challenge)

Madhusudhan, Shashank; Mahurkar, Siddhant; Nagarajan, Suresh Kumar

doi:10.1109/bigmm50055.2020.00074

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Similarly, Wang et al [53] also employed CNN with an attention mechanism. Madhusudhan et al [54] utilized a similar approach, but they used SBERT and ResNet-18. Kang, Hwang, and Yu [55] used MCE, which required corelation between modalities.…”

Section: Multi-modal Approachmentioning

confidence: 99%

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

2023

KSII TIIS

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Social media play a significant role in communicating information across the globe, connecting with loved ones, getting the news, communicating ideas, etc. However, a group of people uses social media to spread fake information, which has a bad impact on society. Therefore, minimizing fake news and its detection are the two primary challenges that need to be addressed. This paper presents a multi-modal deep learning technique to address the above challenges. The proposed modal can use and process visual and textual features. Therefore, it has the ability to detect fake information from visual and textual data. We used EfficientNet-B0 and a sentence transformer, respectively, for detecting counterfeit images and for textural learning. Feature embedding is performed at individual channels, whilst fusion is done at the last classification layer. The late fusion is applied intentionally to mitigate the noisy data that are generated by multi-modalities. Extensive experiments are conducted, and performance is evaluated against state-of-the-art methods. Three real-world benchmark datasets, such as MediaEval (Twitter), Weibo, and Fakeddit, are used for experimentation. Result reveals that the proposed modal outperformed the state-of-the-art methods and achieved an accuracy of 86.48%, 82.50%, and 88.80%, respectively, for MediaEval (Twitter), Weibo, and Fakeddit datasets.

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Section: Multi-modal Approachmentioning

confidence: 99%

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

2023

KSII TIIS

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A Survey of Fake Data or Misinformation Detection Techniques Using Big Data and Sentiment Analysis

kansara,

Adhvaryu

2024

SN COMPUT. SCI.

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Detection of hyperpartisan news articles using natural language processing technique

Naredla

Adedoyin

2022

International Journal of Information Management Data Insights

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Attributional analysis of Multi-Modal Fake News Detection Models (Grand Challenge)

Cited by 4 publications

References 16 publications

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

A Survey of Fake Data or Misinformation Detection Techniques Using Big Data and Sentiment Analysis

Detection of hyperpartisan news articles using natural language processing technique

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