Fake news detection using multilingual evidence

Daryna Dementieva, Alexander Panchenko

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

4 Citations (Scopus)

Abstract

Nowadays, misleading information spreads over the internet at an incredible speed, which can lead to irreparable consequences. As a result, it is becoming more and more essential to combat fake news, especially in the early stages of its origins. Over the past years, a lot of work has been done in this direction. However, all existed solutions have their limitations. One of the main limitations of the current approaches is that the majority of the models are focused only on one language and do not use any multilingual information. In this work, we investigate the new approach of fake news detection based on multilingual evidence. We show effectiveness of the proposed approach in a manual and an automated evaluation experiments.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020
EditorsGeoff Webb, Zhongfei Zhang, Vincent S. Tseng, Graham Williams, Michalis Vlachos, Longbing Cao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages775-776
Number of pages2
ISBN (Electronic)9781728182063
DOIs
Publication statusPublished - Oct 2020
Event7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020 - Virtual, Sydney, Australia
Duration: 6 Oct 20209 Oct 2020

Publication series

NameProceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020

Conference

Conference7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020
Country/TerritoryAustralia
CityVirtual, Sydney
Period6/10/209/10/20

Keywords

  • Fake News Detection
  • Multilingual

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