TEJAS Journal of Technologies and Humanitarian Science

The Impact of social media on the Spread of Fake News and the Role of Machine Learning in Detection

Sushmita Goswami
Saroj Institute of Technology and Management, Lucknow, India

Author

Deepak Kumar Chaubey
Assistant Professor, HOD, Saroj Institute of Technology and Management, Lucknow, India

Author

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Abstract:

The way people consume and share information has been significantly impacted by the proliferation of social media. Access to information has been democratized by social media platforms, but the spread of fake news has also been facilitated by them, which can have serious consequences for individuals and society. In this research paper, the impact of social media on the spread of fake news and the role of machine learning in detecting it are examined. The investigation of the impact of social media on the spread of fake news involves the review of existing literature and the provision of an overview of the different types of fake news and the factors that contribute to its spread. The potential consequences of fake news, including its impact on public opinion and its potential to fuel misinformation and extremism, are then explored. The role of machine learning in detecting fake news is delved into as well. An overview of the different machine learning techniques that have been used to identifying spurious information provided, and their effectiveness is evaluated. The potential biases that can be inherent in machine learning algorithms and the importance of human oversight in ensuring accuracy are also discussed. Finally, case studies of machine learning algorithms that have been developed to identifying spurious information news, including the work of researchers at institutions like MIT and Stanford, are presented. The effectiveness of these algorithms in detecting fake news is evaluated based on metrics such as accuracy and speed. Overall, the importance of understanding the impact of social media on the spread of fake news and the potential of machine learning algorithms to combat this problem is highlighted by our research.

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