Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
This paper showcases a simple deep learning model in combination with word embeddings is employed for the
classification of tweets as human-generated or bot-generated using a publicly available Tweepfake dataset. A conventional
Convolutional Neural Network (CNN) architecture is devised, leveraging Fast Text word embeddings, to undertake the task of
identifying deepfake tweets. To showcase the superior performance of the proposed method, this study employed several machine
learning models as baseline methods for comparison. These baseline methods utilized various features, including Term Frequency,
Term Frequency- Inverse Document Frequency, FastText, and FastText subword embeddings. Moreover, the performance of the
proposed method is also compared against other deep learning models such as Long short-term memory (LSTM) and CNN-LSTM
displaying the effectiveness and highlighting its advantages in accurately addressing the task at hand.
"Deepfake Detection on Social Media", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 4, page no.288 - 297, April-2024, Available :http://www.ijrti.org/papers/IJRTI2404040.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator