Fake news detection using deep learning
By: Katyayani P. N. S
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Material type: 
BookPublisher: ASCI: Journal of Management Description: 51(1), Mar, 2022: p.44-51.Subject(s): Fake news, Deep learning application
In:
ASCI: Journal of ManagementSummary: The pandemic has catalyzed the proliferation of news and data using social media. It has enabled an alternative ecosystem where real-time event reporting is possible. On the flipside, it has also seen the spread of news items that are old or manipulated to suit a current context. Identifying means to detect fake news items in this context is becoming increasingly important. While identifying data quality based on source is one of the sources, media-reported content lacks this feature. The long-standing consequences of fake news have ranged from memes to social unrest. But the disturbing aspect is the rapidness with which such information spreads in a cross-platform model. Several methods have been used to aid in detecting fake news. The techniques range from manual interventions where senior news editors scan to ensure the validity of a news piece to technology-based algorithms to identify fake news. While conventional supervised learning has offered solutions to many traditional problems, the volatility and nature of content are such that there is a need for methods that combine multiple approaches.Deep learning has been a popular tool that many researchers have studied to detect fake news. The algorithms offered by deep learning can provide a robust solution to identify fake news. – Reproduced
| Item type | Current location | Call number | Vol info | Status | Date due | Barcode |
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Articles
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Indian Institute of Public Administration | 51(1), Mar, 2022: p.44-51 | Available | AR128293 |
The pandemic has catalyzed the proliferation of news and data using social media. It has enabled an alternative ecosystem where real-time event reporting is possible. On the flipside, it has also seen the spread of news items that are old or manipulated to suit a current context. Identifying means to detect fake news items in this context is becoming increasingly important. While identifying data quality based on source is one of the sources, media-reported content lacks this feature. The long-standing consequences of fake news have ranged from memes to social unrest. But the disturbing aspect is the rapidness with which such information spreads in a cross-platform model. Several methods have been used to aid in detecting fake news. The techniques range from manual interventions where senior news editors scan to ensure the validity of a news piece to technology-based algorithms to identify fake news. While conventional supervised learning has offered solutions to many traditional problems, the volatility and nature of content are such that there is a need for methods that combine multiple approaches.Deep learning has been a popular tool that many researchers have studied to detect fake news. The algorithms offered by deep learning can provide a robust solution to identify fake news. – Reproduced
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