Detecting Fake News in Social Media in Early Publishing Stages: Deep Fake Detect
Detecting Fake News in Social Media in Early Publishing Stages: Deep Fake Detect |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-7 |
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| Year of Publication : 2025 | ||
| Author : Vedpriya Dongre, Pragya Shukla | ||
| DOI : 10.14445/22315381/IJETT-V73I7P114 | ||
How to Cite?
Vedpriya Dongre, Pragya Shukla, "Detecting Fake News in Social Media in Early Publishing Stages: Deep Fake Detect," International Journal of Engineering Trends and Technology, vol. 73, no. 7, pp.168-178, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I7P114
Abstract
Social media's fake and manipulated content may disturb social harmony and peace. Therefore, identifying and preventing harmful content posting on social media is an essential but complex task. Social media may circulate hateful and fake information as a result of the publication of harmful content. This paper presents a deep learning model using a multi-model feature fusion technique to deal with the harmful content flooding in social media. The proposed model includes the InceptionV3 for deep image feature extraction, and the GloVe pre-trained model has been used to capture the content and contextual features. Next, the extracted features are fused using the concatenation layer, and a stack of dense layers is used with different filter sizes to learn and classify the harmful content to prevent posting on social media. The experiments have been done, and parameters have been tuned to increase the detection capabilities. The model can provide 65% correct recognition of harmful content.
Keywords
Data mining, Deep Learning, Machine Learning, Multi-model feature fusion, Transfer learning, Deep feature learning.
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