The spread of fake news on online media is very dangerous and can lead to casualties, effects on psychology, character assassination, elections for political parties, and state chaos. Fake news that concerning Covid-19 massively spread during the pandemic. Detecting misinformation on the Internet is an essential and challenging task since humans face difficulty detecting fake news. We applied BERT and GPT2 as pre-trained using the BiGRU-Att-CapsuleNet model and BiGRU-CRF features augmentation to solve Fake News detection in Constraint @ AAAI2021 - COVID19 Fake News Detection in English Dataset. This research proved that our hybrid model with augmentation got better accuracy compared to our baseline model. It also showed that BERT gave a better result than GPT2 in all models; the highest accuracy we achieved for BERT is 0.9196, and GPT2 is 0.8986.

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 Counterfeit Generator, to increase detection accuracy using deep learning. This approach plays an 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 counterfeit training data using BART, and comparing counterfeit-generated text toward source text using cosine similarity. If the comparison value between counterfeit-generated text and source text is more than 95%, then add that counterfeit-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.

Fake news is false or misleading information presented as news. False stories tend to spread farther, faster, and more broadly than true stories. Some individuals and organizations intentionally applied misleading information for certain benefits. The internet goes quickly, mobile devices became popular, and social media users grow more significant in number, make no boundaries world and connect every user. As a result, information circulates in immense numbers makes to handle fake news manually is impossible. Fake news also affects human psychology. Hoax related to how to cure COVID-19 and the lack of toilet paper has led to casualties and riots. Fake news potentially differs in writing technique compare to real information. In this paper, we discussed a few studies related to fake news detection. Their algorithm can capture writing style in content viz lexicon, syntax, semantic, and discourse. Also, those studies implemented user-based, content-based, and social context-based. It used machine learning, deep learning, and reinforcement learning. Furthermore, education plays a vital role in making people understand the impact of fake news. Itis necessary to do a campaign for all internet users. The national government necessity collaborates with society implement the particular rule for better internet environment.

Preventing Fake News in Formal Education Way