@inproceedings{64df9a974a834bfa9aaaf1170bb44e94,
title = "Machine Learning based Fake News Detection using linguistic features and word vector features",
abstract = "Nowadays on the internet, lots of information is spread every second by the people. On social media, most of the users do not verify the information and propagate it. Manually identifying fake news is a very tremendous problem for all. So, the need for an automatic system that efficiently detects fake news. This paper estimated a model that intuitionally distinguishes fake news from a news article. A new feature set for machine learning classifier has been proposed. Within the experiment, the dataset used has a combination of two datasets that contain equal true news and fake news articles of politics. From text fields of the dataset extract linguistic/stylometric features, a bag of words TF and BOW TF-IDF vector, after that apply the various machine learning models including bagging and boosting methods to achieve the best accuracy.",
keywords = "BOW TF, BOW TF-IDF, Fake news, Machine learning, Social media, Stylometric/linguistic features",
author = "{Kumar Jain}, Mayank and Dinesh Gopalani and {Kumar Meena}, Yogesh and Rajesh Kumar",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 7th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2020 ; Conference date: 27-11-2020 Through 29-11-2020",
year = "2020",
month = nov,
day = "7",
doi = "10.1109/UPCON50219.2020.9376576",
language = "English",
series = "7th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "7th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2020",
address = "United States",
}