Reinforcement Learning for Fake News Detection on Social Media with Blockchain Security

Information sharing on social media, especially about daily news and events, is a major focus area. Timely identification of urgent needs, sharing relevant posts, and delivering accurate information are crucial tasks. To combat the spread of fake news, a Reinforcement Learning (RL) technique is used alongside blockchain security to verify social media content. Twitter, a key platform with a major influence on public discourse, is particularly susceptible to false information due to its rapid news dissemination. The approach involves collecting news articles and their metadata, which are then pre-processed to clean and tokenize the data. An RL agent is trained on attributes like word frequency and readability, learning to distinguish between genuine and fake news through rewards and penalties. The trained RL agent classifies new news as true or false based on learned patterns. While blockchain's role in enhancing security is highlighted, further details are necessary to clarify its integration. This approach aims to reduce the spread of misinformation in digital news effectively.

Перевод пока недоступен