Abstract
Fake news is news articles providing wrong information to readers. Normally these articles are originated from social networking sites which immensely influence young generation as well as various sectors of life. Among the few biggest sources of rumors, fake news and fake information are social networking sites like Facebook and Twitter. Detection of fake news on Twitter is challenging as the amount of resources is limited; less datasets are available, and also a large amount of data is generated by these social networking sites. It is very difficult to predict if the data is fake or not even after having knowledge in the same area. In this report, we explained a method for detection of fake news on Twitter which involves collecting livestream data of Twitter using flumes and performing N-gram analysis on it for feature extraction and then applying decision tree machine learning technique to classify documents as fake or real.
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Acknowledgements
I am thankful to Malaya Sir and Ritesh Shinde sir for their time and insightful tips in the work.
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Keskar, D., Palwe, S., Gupta, A. (2020). Fake News Classification on Twitter Using Flume, N-Gram Analysis, and Decision Tree Machine Learning Technique. In: Bhalla, S., Kwan, P., Bedekar, M., Phalnikar, R., Sirsikar, S. (eds) Proceeding of International Conference on Computational Science and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0790-8_15
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