TY - GEN
T1 - Maithi-Net
T2 - 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
AU - Muduli, Debendra
AU - Sharma, Santosh Kumar
AU - Kumar, Dinesh
AU - Singh, Akshat
AU - Srivastav, Shubham Kumar
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Online news consumers now face serious difficulties due to the widespread distribution of fake news on social media platforms. To distinguish fake news from real, the paper suggested a customized CNN model named "Maithi-Net". The model being suggested is composed of five convolution layers which are capable of automatically acquiring the distinguishing features essential for identifying fake news. Both the CGU-Maithili and ISOT fake news datasets have been used to successfully validate the proposed model. The efficacy of the model is verified with several evolution metrics like accuracy, specificity, sensitivity and F1 score. The model provides the detection accuracy 96.85 % for CGU-Maithili and 97.28% for ISOT fake news datasets. The experimental results show substantial gains over prior state-of-the-art results in the area of fake news detection and validate the potential of our method for categorising misinformation spread via social media.
AB - Online news consumers now face serious difficulties due to the widespread distribution of fake news on social media platforms. To distinguish fake news from real, the paper suggested a customized CNN model named "Maithi-Net". The model being suggested is composed of five convolution layers which are capable of automatically acquiring the distinguishing features essential for identifying fake news. Both the CGU-Maithili and ISOT fake news datasets have been used to successfully validate the proposed model. The efficacy of the model is verified with several evolution metrics like accuracy, specificity, sensitivity and F1 score. The model provides the detection accuracy 96.85 % for CGU-Maithili and 97.28% for ISOT fake news datasets. The experimental results show substantial gains over prior state-of-the-art results in the area of fake news detection and validate the potential of our method for categorising misinformation spread via social media.
KW - CNN
KW - Fake News
KW - Logistic Regression
KW - Naive Bayes
KW - Recurrent Neural Network
UR - http://www.scopus.com/inward/record.url?scp=85174520400&partnerID=8YFLogxK
U2 - 10.1109/IC2E357697.2023.10262664
DO - 10.1109/IC2E357697.2023.10262664
M3 - Conference contribution
AN - SCOPUS:85174520400
T3 - 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
BT - 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 June 2023 through 9 June 2023
ER -