Fake News Detection System Using Featured-Based Optimized MSVM Classification

Abstract

Fake News creates erroneous suspense information that can be identified. This spreads dishonesty about a country's status or overstates the expense of special functions for a government, destroying democracy in certain countries, such as in the Arab Spring. Associations such as the ''House of Commons and the Crosscheck project'' address concerns such as publisher responsibility. However, since they rely entirely on manual detection by humans, their coverage is minimal. This is neither sustainable nor possible in a world where billions of items are withdrawn or posted every second. The paper produces a deep study on past research work on fake news detection on the selected data-sets and proposes an algorithm with Multi-layered Principal Component Analysis for feature selection followed by firefly-optimized algorithm. Multi-Support Vector Machines(MSVM) are finally used to classify the news. We used ten different data-sets for testing the proposed algorithm. As the number of features in the data-sets are more, feature extration and selection methods help to improve the accuracy in respective data-sets. Only the datasets having less number of features gave a lower performance on our feature extraction algorithms. INDEX TERMSFake news, rirefly, long short-term memory (LSTM), multi support vector machine (MSVM).

Deep Learning Based Semantic Model for Multimodal Fake News Detection

“…Most existing research on fake news detection utilizes textual content and the social context generated during the dissemination process [9]. Textbased detection methods primarily rely on modelling the specific language style associated with fake news, including early approaches that extract linguistic features, topic features, and other handcrafted features [10][11][12][13], as well as more recent methods that leverage deep models to automatically learn highlevel features from the data [10] on user behaviour credibility [14][15][16] and methods based on the propagation network [17][18][19][20]. In recent years, some studies have started to focus on the role of visual modality in fake news detection [21][22][23][24][25].…”

Multimodal fake news detection using hyperparameter-tuned BERT and ResNet110

“…Social media has aided users to acquire news, indicate perspectives and communicate personal judgements with others [2]. The huge evolution of web technology has made it possible for users to post both real and fake news on social media [3,4]. The blogs, headlines and social media messages are deliberately put forward as ambiguous for various reasons [5].…”