| Autor: |
M. K., Jayashree, Ojha, Ananta Charan, J., Adlin |
| Předmět: |
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| Zdroj: |
IUP Journal of Information Technology; Sep2021, Vol. 17 Issue 3, p49-60, 12p |
| Abstrakt: |
The internet has changed dramatically the way people engage with one another. Social media websites and instant messaging applications on the internet have connected people 24/7. These media spread stories and opinions at a speed on the internet faster than traditional media. They allow people throughout the world to participate in a near-real-time deliberation on both trivial and nontrivial topics. Today, with easy access to a wide range of internetenabled mobile devices and affordable data bandwidth, millions of people are using these media. A lot of people acquire news from websites and social media platforms more frequently than ever before. Often, many of the news items available on these platforms are misleading and far from reality. It has a significant impact on the decision-making and behavior of people. Since misinformation spreads very fast as compared to facts, on-time identification of fake news and its control is essential in the era of the internet and social media. The paper presents fake news detection approaches and automated detection and classification of fake news articles by extracting useful linguistic features from textual data. Several machine learning algorithms have been used to study their performance on a real-world news article corpus. It has been found that almost all machine learning models considered in the study show very encouraging results in classifying fake news. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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