Multi-Modal Analysis of Misleading Political News

Anu Shrestha, Francesca Spezzano, Indhumathi Gurunathan

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The internet is a valuable resource to openly share information or opinions. Unfortunately, such internet openness has also made it increasingly easy to abuse these platforms through the dissemination of misinformation. As people are generally awash in information, they can sometimes have difficulty discerning misinformation propagated on these web platforms from truthful information. They may also lean too heavily on information providers or social media platforms to curate information even though such providers do not commonly validate sources. In this paper, we focus on political news and present an analysis of misleading news according to different modalities, including news content (headline, body, and associated image) and source bias. Our findings show that hyperpartisan news sources are more likely to spread misleading stories than other sources and that it is not necessary to read news body content to assess its validity, but considering other modalities such as headlines, visual content, and publisher bias can achieve better performances.

Original languageAmerican English
Title of host publicationDisinformation in Open Online Media: Second Multidisciplinary International Symposium, MISDOOM 2020, Leiden, The Netherlands, October 26-27, 2020, Proceedings
StatePublished - 1 Jan 2020

Keywords

  • misinformation detection on the web
  • multi-modal content analysis
  • source bias

EGS Disciplines

  • Computer Sciences

Fingerprint

Dive into the research topics of 'Multi-Modal Analysis of Misleading Political News'. Together they form a unique fingerprint.

Cite this