The gray side of fake news: A multiclass approach to detecting fake news, real news and everything else in between

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Content verification studies aim to employ methodologies to identify deceptive contents on social media. Some of the proposed methods include the use of source credibility, source rating and detection. Unlike other deceptive instances on online platforms like review manipulations, where the outcome variables are dichotomous, fake news and real news do not exist in isolation. However, studies on content verification have since treated the concept of fake news as a binary classification task. We bridge the gap in literature by analyzing fake versus real news and then introducing a third class into our nomology. We propose an innovative hybrid approach based on a multi-class algorithm that leverages the use of stacked gradient boosting ensemble and random forests to detect false, real and tweets that can be classified as “noise”. We show that although a classifier's efficacy may suffer after the introduction of the third class, its overall utility is improved.

Original languageEnglish (US)
Title of host publication26th Americas Conference on Information Systems, AMCIS 2020
PublisherAssociation for Information Systems
ISBN (Electronic)9781733632546
StatePublished - 2020
Externally publishedYes
Event26th Americas Conference on Information Systems, AMCIS 2020 - Salt Lake City, Virtual, United States
Duration: Aug 10 2020Aug 14 2020

Publication series

Name26th Americas Conference on Information Systems, AMCIS 2020

Conference

Conference26th Americas Conference on Information Systems, AMCIS 2020
Country/TerritoryUnited States
CitySalt Lake City, Virtual
Period8/10/208/14/20

Keywords

  • Detection
  • Fake news
  • Multiclass
  • Random forest
  • Stacked-ensemble

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Computer Networks and Communications
  • Library and Information Sciences

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