Machine Learning based Fake News Detection using linguistic features and word vector features

Mayank Kumar Jain, Dinesh Gopalani, Yogesh Kumar Meena, Rajesh Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Citations (Scopus)

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.

Original languageEnglish
Title of host publication7th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738111513
DOIs
Publication statusPublished - 7 Nov 2020
Externally publishedYes
Event7th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2020 - Prayagraj, Uttar Pradesh, India
Duration: 27 Nov 202029 Nov 2020

Publication series

Name7th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2020

Conference

Conference7th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2020
Country/TerritoryIndia
CityPrayagraj, Uttar Pradesh
Period27/11/2029/11/20

Keywords

  • BOW TF
  • BOW TF-IDF
  • Fake news
  • Machine learning
  • Social media
  • Stylometric/linguistic features

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Instrumentation

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