Fake News Detection Using Recurrent Neural Network in Somali Language
Fake News Detection Using Recurrent Neural Network in Somali Language |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-9 |
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| Year of Publication : 2024 | ||
| Author : Ubaid Mohamed Dahir, Abdirahman Osman Hashi, Abdullahi Ahmed Abdirahma, Mohamed Abdirahman Elmi, Siti Zaiton Mohd Hashim |
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| DOI : 10.14445/22315381/IJETT-V72I9P139 | ||
How to Cite?
Ubaid Mohamed Dahir, Abdirahman Osman Hashi, Abdullahi Ahmed Abdirahma, Mohamed Abdirahman Elmi, Siti Zaiton Mohd Hashim, "Fake News Detection Using Recurrent Neural Network in Somali Language," International Journal of Engineering Trends and Technology, vol. 72, no. 9, pp. 419-427, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I9P139
Abstract
The proliferation of fake news in the digital domain poses a significant threat to public discourse, necessitating the development of effective detection mechanisms. Therefore, this paper presents an empirical analysis of a Recurrent Neural Network (RNN) model tailored for the detection of fake news, offering an in-depth examination of its performance on a testing dataset. The RNN model demonstrated exceptional accuracy, achieving a 98.94% success rate in accurately distinguishing between fake and real news articles, with a low loss value of 0.0372, indicating high precision in classification tasks. Key performance metrics further elucidate the model's capabilities: a precision rate of approximately 98.73% underscores the model's accuracy in identifying fake news. In comparison, a recall rate of about 99.07% highlights its proficiency in correctly classifying a majority of fake news instances within the dataset. The synthesis of these results—accuracy, precision, and recall—attests to the robustness of the RNN model as a highly reliable tool for discriminating between genuine and fabricated news content. These findings not only reinforce the model's applicability in real-world scenarios, crucial for filtering misinformation but also underscore its potential in maintaining informational integrity. This study paves the way for future research and application in misinformation detection, signifying a substantial contribution to the field.
Keywords
Fake News Detection, Recurrent Neural Network (RNN), Machine Learning, Natural Language Processing (NLP), Adaptive Algorithms, Real-time Analysis.
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