MFF-LSTM: Çok Ölçekli Özellik Füzyon Tabanlı Uzun Kısa TasarlamaYalan Haber Tespit Sistemi için Farklı Özelliklere Sahip Dönem Belleği

The rise in the usage of media has led to a significant increase in the raise of false information, making it imperative to combat this issue and reduce our reliance on such unreliable sources. Fake news can mislead people, spread rumors, and even impact the positions of political leaders. Detecting fake news has become crucial in this digital era, with direct messaging platforms and social media playing a major role in its proliferation. Various innovative techniques have been suggested to determine fake news, making the endeavor both intriguing and challenging. Hence, this synopsis aims to develop the adaptive learning model with multiscale feature fusion for fake news detection. The proposed system constitutes "text collection, text pre-processing, feature extraction and detection". Initially, the text input is collected from the benchmark datasets, which is then followed by the text stage of pre-processing. Here, the pre-processed text is obtained that is fed into the feature extraction phases. The three feature extraction techniques such as "Bidirectional Encoder Representations from Transformers (BERT), Term Frequency-Inverse Document Frequency (TF-IDF) and GloVe Embedding" are employed to provide the feature set 1, 2 and 3. Finally, these resultant features are given to "Multiscale Feature Fusion based Long Short Term Memory (MFF-LSTM)", where the features are fused together in multiscale manner and detection is taken place by LSTM. Therefore, the system evaluation is done by considering the distinct measures and compared among traditional approaches. Hence, the recommended model attains the desired results to detect the fake news that helps to evade the exploration of any false information.