TB-BCG: Topic-Based BART copyright Generator for Fake News Detection

TB-BCG: Topic-Based BART copyright Generator for Fake News Detection

Blog Article

Fake news has been spreading intentionally and misleading society to believe unconfirmed information; this phenomenon makes it challenging to identify fake news based on shared content.Fake news circulation is not only a current issue, but it has been disseminated for centuries.Dealing with fake news is a challenging task because it spreads massively.

Therefore, automatic fake news detection is urgently needed.We introduced TB-BCG, Topic-Based BART copyright Generator, to increase detection accuracy using deep learning.This approach plays an lovesense 3 essential role in selecting impacted data rows and adding more training data.

Our research implemented Latent Dirichlet Allocation (Topic-based), Bidirectional and Auto-Regressive Transformers (BART), and Cosine Document Similarity as the main tools involved in Constraint @ AAAI2021-COVID19 Fake News Detection dataset shared task.This paper sets forth this simple yet powerful idea by selecting a dataset based on topic and sorting based on distinctive data, generating copyright training data using BART, argan oil pure purple and comparing copyright-generated text toward source text using cosine similarity.If the comparison value between copyright-generated text and source text is more than 95%, then add that copyright-generated text into the dataset.

In order to prove the resistance of precision and the robustness in various numbers of data training, we used 30%, 50%, 80%, and 100% from the total dataset and trained it using simple Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN).Compared to baseline, our method improved the testing performance for both LSTM and CNN, and yields are only slightly different.