Fake News Detection Models

This project focuses on detecting fake news within articles using a variety of supervised machine learning models, including Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Transformers.

Results

KAGGLE dataset results:

	accuracy	precision	recall	f1
PassiveAggressiveClassifier	0.99443	0.99532	0.993	0.99416
LogisticRegression	0.98664	0.98646	0.98554	0.986
MultinomialNB	0.93964	0.94112	0.9319	0.93649
KNeighborsClassifier	0.65612	0.95872	0.29244	0.44818
RandomForestClassifier	0.9902	0.98883	0.99067	0.98975
LinearSVC	0.99465	0.99579	0.993	0.9944

WELFake dataset results:

	accuracy	precision	recall	f1
PassiveAggressiveClassifier	0.95992	0.95307	0.96892	0.96093
LogisticRegression	0.94397	0.93197	0.95992	0.94574
MultinomialNB	0.86907	0.87051	0.87241	0.87146
KNeighborsClassifier	0.6301	0.58095	0.97928	0.72927
RandomForestClassifier	0.93329	0.91945	0.95229	0.93558
LinearSVC	0.96117	0.95173	0.97301	0.96225
RNN	0.98767	0.98829	0.98827	0.98828
CNN	0.98706	0.98662	0.98652	0.98657
Transformer	0.97152	0.97149	0.97149	0.97149

LIAR dataset results:

	accuracy	precision	recall	f1
PassiveAggressiveClassifier	0.23362	0.23245	0.23362	0.23303
LogisticRegression	0.25019	0.24541	0.25019	0.24778
MultinomialNB	0.23757	0.23089	0.23757	0.23418
KNeighborsClassifier	0.21863	0.21483	0.21863	0.21671
RandomForestClassifier	0.25651	0.26152	0.25651	0.25899
LinearSVC	0.24546	0.24380	0.24546	0.24463
RNN	0.24226	0.22991	0.24073	0.23519
CNN	0.21354	0.20257	0.21231	0.20732
Transformer	0.21078	0.16977	0.21073	0.18804

LIAR dataset with party and speaker results:

	accuracy	precision	recall	f1
PassiveAggressiveClassifier	0.22889	0.23023	0.22889	0.22956
LogisticRegression	0.25572	0.26227	0.25572	0.25895
MultinomialNB	0.25493	0.26958	0.25493	0.26205
KNeighborsClassifier	0.22257	0.23144	0.22257	0.22692
RandomForestClassifier	0.24625	0.25599	0.24625	0.25103
LinearSVC	0.23994	0.24102	0.23994	0.24048

Binary LIAR dataset results:

	accuracy	precision	recall	f1
PassiveAggressiveClassifier	0.56196	0.60935	0.62045	0.61485
LogisticRegression	0.61563	0.63182	0.76190	0.69079
MultinomialNB	0.60142	0.60336	0.85434	0.70725
KNeighborsClassifier	0.57064	0.60732	0.67367	0.63878
RandomForestClassifier	0.60852	0.62415	0.76751	0.68844
LinearSVC	0.60221	0.63636	0.68627	0.66038
RNN	0.58626	0.56522	0.07052	0.12540
CNN	0.52904	1.0	0.00181	0.00361
Transformer	0.56380	0.0	0.0	0.0
Transformer-Pretrained	0.50185	0.37963	0.22242	0.28050