dc.description.abstract |
With the advancement of technology, fake news has become a common trend. It has the
ability to cause harm to citizens, protests, and even a threat to democracy. It gives
agitators the ability to spread fake news or false propaganda, triggering civil unrest. Since
then, identifying fake news has become a critical concern, with every company
attempting to develop a system for detecting and combating it. Fake news has had a
negative impact on society in terms of politics and culture. It has had a negative impact
on both online and offline social networking networks, as well as groups and
conversations. In this study, I propose a model for detecting false news that combines
NLP and machine learning techniques. To determine which model produces the best
results, I compare various classifier models and extraction techniques. Introduction,
motivation, rationality of the study, research questions, expected output, terminologies,
related works, comparative analysis, challenges, research methodology, statistical
analysis, applied mechanism, results and discussion are briefly written here. Impact on
society, environment and sustainability are also explained here. Tables and Figures are
included and listed. Then pertinent information is repeated in the summary section for
convenience. At the end of the report an annotated reference list is included for ease in
finding other useful guidance. |
en_US |