Long-Short-Term Memory Model for Fake News Detection in Nigeria
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Date
2023
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Ianna Journal of Interdisciplinary Studies
Abstract
Background: The advent of technology allows information to be passed through the Internet at a
breakneck speed and enables the involvement of many individuals in the use of different social
media platforms. Propagation of fake news through the Internet has become rampant due to
digitalisation, and the spread of fake news can cause irreparable damage to the victims. The
conventional approach to fake news detection is time-consuming, hence introducing fake news
detection systems. Existing fake news detection systems have yielded low accuracy and are
unsuitable in Nigeria.
Objective: This research aims to design and implement a framework for fake news detection
using the Long-Short Term Memory (LSTM) model.
Methodology: The dataset for the model was obtained from Nigerian dailies and Kaggle and
pre-processed by removing punctuation marks and stop words, stemming, tokenisation and one
hot representation. Feature extraction was done on the datasets to remove outliers. The locally
acquired dataset from Nigeria was balanced using Synthetic Minority Oversampling Techniques
(SMOTE) Long-Short Term Memory (LSTM), a variant of Recurrent Neural Network (RNN)-
which solved the problem of losing gained knowledge and information over a long period faced
by RNN- was used as the detection model This model was implemented using Python 3.9. The
model detected fake news by classifying real and fake news approaches. The dataset was fed into
the model, and the model classified them as either fake or real news by processing the dataset
through input and hidden layers of varying numbers of neurons. accuracy F1 score and detection
time were used as the evaluation metrics. The results were then compared to some selected
machine learning models and a hybrid of convolutional neural networks and long short-term
memory models (CNN-LSTM).
Results: The result shows that the LSTM model on a balanced dataset performed best as the two
news classes were accurately classified, giving an average detection accuracy of 92.86%, which
took the model 0.42 seconds to detect whether news was real or fake. Also, 87.50% average
detection accuracy was obtained from an imbalanced dataset. Compared to other machine
learning models, SVM and CNN-LSTM gave 81.25% accuracy for imbalanced datasets and
82.14% and 78.57% for balanced datasets, respectively.
Conclusion: The outcome of this research shows that the deep learning approach outperformed
some machine learning models for fake news detection in terms of performance accuracy.
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Citation
Adebimpe Esan, Olayinka Abodunrin, Adedayo Sobowale, Ibrahim Adeyanju, Nnamdi Okomba, Bolaji Omodunbi, Tomilayo Adebiyi, Janet Jooda, Taofeek Abdul-Hameed and Opeyemi Asaolu (2023). Long-Short-Term Memory Model for Fake News Detection in Nigeria, Ianna Journal of Interdisciplinary Studies, Vol. 5, Pp. 167 – 180