Abstract:
The influence of social media on individuals and their lives is significant. Numerous news
blogs, articles, and other forms of content are regularly disseminated to users through
social media platforms. Social media has emerged as a valuable and rapid source of
updates. However, the exponential growth in the use of social media and the ease of
information sharing have led to a proliferation of dubious and misleading content on
the internet. Distinguishing between authentic and fake news has become challenging,
as some fabricated news closely resembles genuine news. Consequently, it is crucial
to employ automated tools such as machine learning and deep learning techniques for
the detection of fake news. In this study, we have employed six models from machine
learning and three from deep learning using the ISOT dataset that contains both the
fake news and the real ones. For text representation in the machine learning models,
we utilized TF-IDF and Bag of Words techniques. The ISOT fake news dataset was
utilized for this research. As far as the evaluation is concerned, we used metrics such
as recall, precision, accuracy and F1 score. Our proposed GRU model shows a 100%
testing accuracy for ISOT dataset, along with 100% recall, precision, and F1 score.
