Fake News Detection using Machine Learning

Fake News Detection Using Machine Learning Thesis Ideas

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1. Deep vs. Shallow: A Comparative Study of Machine Learning and Deep Learning Approaches for Fake Health News Detection

Keywords

Fake news, healthcare, classification, deep learning, machine learning, readability features

            Two innovative models are recommended in our approach for fake news identification named Content Based Models (CBM) and Feature Based Models (FBM). We applied various ML methods such as DT, RF, SVM, AdaBoost-Decision Tree and AdaBoost-Random Forest and we compared these methods with integrated DL methods like CNN-LSTM and CNN-BiLSTM on each model. Results show that, AdaBoost-Random Forest in FBM offers greatest outcomes.

2. Constructing a User-Centered Fake News Detection Model by Using Classification Algorithms in Machine Learning Techniques

Keywords

Classification algorithms, fake news detection, feature selection, prediction algorithms, predictive models, XGBoost.

            A fake news identification framework is suggested in our paper through the utilization of ML techniques. We obtained the relevant factors by evaluating the feature importance of each attribute by employing XGBoost technique. We employed and compared various methods such as SVM, RF, LR, CART, and NNET for fake news recognition. We also carried out the cross-validation step. From the analysis, we conclude that, RF method achieved better end results.

3. Thai Fake News Detection Using Machine Learning Model

Keywords

Social media, support vector machine (SVM), neural network (NN), naive bayes (NB)

            By employing ML techniques, we recommended a fake news recognition system in our study. Here we utilized various methods like Support Vector Machine, Naive Bayes, and Neural Network. Before applying ML methods, we performed normalization procedure to maintain the same dimension among all data. As a consequence, Support Vector Machine outperformed others.

4. Detection of Fake News using Machine Learning

Keywords

Natural Language Processing (NLP), Natural Language Toolkit (NLTK), Term Frequency-Inverse Document Frequency (tf-idf) Vectorizer, Ln-built and Custom ensembled Machine Learning (ML) Models, Support Vector Classifier (SVC), Logistic Regression (LR), K- Nearest Neighbors (KNN)

            To categorize the news as legitimate or illegitimate, we developed a framework by using several ML approaches. For implementation, we utilized python as a scripting language. We employed ML methods like KNN and DT with an integrated approach comprised of RF and GB and custom ensembled methods like Stacking, and Maximum Voting Classifier for fake news identification. Stacking of methods like KNN, SVC, and LR into custom ensembles method generated highest efficiency in news categorization process.

5. Fake News Detection Using Machine Learning Techniques

Keywords

Classifier, Decision Tree, Random Forest, Gradient Boosting

            A ML based framework is suggested in our article for false news recognition. We preprocessed the data by eliminating punctuation, tokenization, special characters, empty spaces, repeated words etc. and carried out stemming process and data discretization. We utilized several techniques such as LR, DT, RF and GB to detect false news from new articles. We conclude that, DT technique provides better outcomes when compared to others.

6. Detecting Fake News Using Machine Learning Based Approaches

Keywords

Multilayer perceptron, ensemble learning

            A main concentration of our study is to build an autonomous model for false news recognition by utilizing natural language processing of news texts. To forecast whether the news is false or not, we employed ML based categorization approaches. We evaluated and compared various techniques like LR, SVM, DT, KNN, multinomial NB, MLP and an ensemble model known as stacking. We state that, ensemble learner, SVM and MLP generates better efficiency.

7. Machine Learning based Novel Framework for Fake News Detection and Prevention using Blockchain

Keywords

Blockchain, Rumour detection, InterPlanetary File system, Smart Contract

            An innovative model utilizing ML method and blockchain is recommended in our research to identify the false news. We examined the authenticity of the information by evaluating the ratings given by normal people and experts about the information. To make the distributed framework, we utilized Ethereum blockchain. To stock the data, we employed InterPlanetary File System. We utilized blockchain to stock the hash of the metadata.

8. Fake News Detection Using Machine Learning

Keywords

Supervised Learning Models, Ensemble Model, Evaluation Metrics

            A constructive recruitment fraud identification system is suggested in our paper by utilizing various ML techniques. To examine the job posting news like whether it is true or fake, we employed methods such as SVM, RF and NB. We utilized approaches like TF-IDF and BoW to extract the features. By training three separate methods, we developed an integrated approach. Finally, we conclude that, RF method provides effective end results.

9. Fake news detection on Pakistani news using machine learning and deep learning

Keywords

BERT, GloVe, LSTM, CNN

            By utilizing multiple fact-checked news APIs, we created a dataset for fake news identification. We employed several ML approaches like NB, KNN, LR, SVM and DT to examine the created dataset. Then we utilized DL methods such as CNN and LSTM with GloVe and BERT embeddings. We performed experimental analysis for all these utilized techniques in terms of various metrics. From the analysis, we state that, LSTM with GloVe embeddings offers efficient results.

10. COVID-19 Fake News Detection Model on Social Media Data Using Machine Learning Techniques

Keywords

            In our fake news detection approach, we considered the covid-19 based information for classification process. We extracted markers from unstructured text information. Then we selected only the relevant features to minimize the system complexity. We employed different ML methods like SVM, NB, and DT. We carried out the comparative analysis in terms of various metrics. Results show that, DT method achieved greater outcomes.