Browsing by Author "Padmavathy L."

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    LEVY FLIGHT-BASED DOVE SWARM OPTIMIZATION AND DEEP NEURAL NETWORK FOR FAKE NEWS DETECTION IN SOCIAL MEDIA (Conference Paper)
    (Springer Science and Business Media Deutschland GmbH, 2024-05-23) Padmavathy L.; Radha N; Nithya S
    By disseminating fake information, fake news plays a significant part in influencing people’s knowledge and perceptions as well as their decision-making. By incorporating real news into fake news, online forums and social media have encouraged its dissemination. Fake news has become the primary obstacle to having a greater impact in the information-driven environment for determined fakers. Due to a number of features in the dataset, testing on a single dataset in the current system may produce false results. The performance of classifying fake news is reduced. This study introduces the Levy flight-based dove swarm optimization (LDSO) and deep neural network (DNN) algorithms for the detection of fake news. The preprocessing, news-user engagement matrix design, feature selection, and fake news classification method are the primary phases of this effort. Preprocessing is carried out using stemming, stop word removal, and tokenization on the features from BuzzFeed and PolitiFact datasets. It is used to eliminate extraneous features to increase the accuracy of predicting fake news. The news-user engagement matrix is then constructed for detection. Latent representation of together the news content and the social context is obtained with tensor by a coupled matrix-tensor factorization algorithm. These features are included in the process of feature selection, which is carried out by the LDSO algorithm. Then DNN method is used to classify fake news by dealing with a variety of filters across every dense layer by dropout. Based on the experimental findings, it was determined that the proposed LDSO-DNN algorithm outperforms the existing methods by increased precision, recall, F-measure, and accuracy.
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    STUDY ON MACHINE LEARNING AND DEEP LEARNING FOR FAKE NEWS DETECTION (FND) IN SOCIAL MEDIA (Conference Paper)
    (Springer Science and Business Media Deutschland GmbH, 2025-02-25) Padmavathy L.; Radha N
    Due to the risks associated with fake news, gathering fake information through social networks is difficult. As a result, it is now extremely difficult to evaluate fake news so that the producers can verify it through data processing media without misleading the public. There is a need for an automated technique for the detection because the news’s technical quality is in consideration. Studies currently in existence generally concentrate on using information extracted from the news content. This review’s objective is to use machine learning and deep learning techniques to clearly discuss FND on social media. (1) FND in social media by machine learning and (2) FND in social media by deep learning in data mining are the two main headings in this paper review. Lastly, the finest FND for effective results is deep learning. A Deep Neural Network (DNN) used by the best hyper-parameters has been introduced to categorize news and social context separately. FND dataset from BuzzFeed and PolitiFact by Kaggle repository has been used to validate the effectiveness of the proposed approach. Precision, recall, F-measure, and accuracy are some of the parameters applied to test the validity of the proposed model.