Exploration of Online Fake News Through Machine Learning and Sentiment Analyses
Lecture Notes in Networks and Systems, ISSN: 2367-3389, Vol: 569 LNNS, Page: 439-448
2023
- 1Citations
- 4Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
With the progress of technology throughout the world, the rapid adoption of online platforms is rising daily. It is quickly becoming a primary source of information, and we rely on it to get the latest news in the simplest possible way. However, many different forms of news are available online, and it might not be easy to recognize and pick reliable news. This paper uses supervised learning to recognize false news using four machine learning algorithms: Logistic Regression, Decision Tree, Gradient Boosting, and Random Forest, simultaneously on a valid dataset. We conducted a performance study of these prediction models and discovered that Logistic Regression and Gradient Boosting perform well on the dataset. Besides, we evaluate false and real online news sentiments to find the distinctions between these two forms of news in our relative contribution. This paper incorporates all of our unique observations than that of other ones. Furthermore, our outcomes are concrete, precise, and significant.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85144573321&origin=inward; http://dx.doi.org/10.1007/978-3-031-19958-5_41; https://link.springer.com/10.1007/978-3-031-19958-5_41; https://dx.doi.org/10.1007/978-3-031-19958-5_41; https://link.springer.com/chapter/10.1007/978-3-031-19958-5_41
Springer Science and Business Media LLC
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