| 000 -LEADER |
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| fixed length control field |
01784nam a22001577a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230309b ||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Katyayani P. N. S. |
| 245 ## - TITLE STATEMENT |
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| Title |
Fake news detection using deep learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
ASCI: Journal of Management |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
51(1), Mar, 2022: p.44-51 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
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 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Fake news, Deep learning application. |
| 9 (RLIN) |
36664 |
| 773 ## - HOST ITEM ENTRY |
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| Main entry heading |
ASCI: Journal of Management |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
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| Subject DIP |
MASS MEDIA |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Item type |
Articles |
