Abstract:
Recent years have seen an explosion in social media usage, allowing people to connect
with others. Since the appearance of platforms such as Facebook and Twitter, such
platforms influence how we speak, think, and behave. This problem negatively undermines
confidence in content because of the existence of fake news. For instance, false news was
a determining factor in influencing the outcome of the 2016 presidential election. Because
this information is so harmful, it is essential to make sure we have the necessary tools to
detect and resist it. It's difficult to determine what news is false and what is true. We've
hardly put in any effort for such a high-quality outcome. This work is for analyzing &
delectating the fake news from a fresh collected dataset. We applied Bidirectional Long
Short-Term Memory (BiLSTM) to determine if the news is false or real in order to
showcase this study. This machine learning technique and approach are being used since
there is a lot of study into how people can improve the efficiency and accuracy of their
work. A number of foreign websites and newspapers were used for data collection. After
creates & running the model, the work achieved 84% model accuracy and 62.0 F1-macro
score with training data.
