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Disinformation: analysis and identification.

Archita Pathak1,2, Rohini K Srihari1, Nihit Natu1,3

  • 1University at Buffalo (SUNY), 338D Davis Hall, Buffalo , NY 14260-2500 USA.

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Summary
This summary is machine-generated.

This study explores automated disinformation detection, analyzing linguistic clues and headline clickbait. Findings show potential for automated fact-checking to combat harmful, false information.

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Area of Science:

  • Computational Linguistics
  • Information Science
  • Digital Forensics

Background:

  • Disinformation, defined as intentionally shared false and misleading information causing harm, poses a significant challenge.
  • The study contextualizes disinformation within major events like the 2016 US elections and the 2020 COVID-19 pandemic.
  • Existing methods struggle to differentiate disinformation from legitimate news due to overlapping characteristics.

Purpose of the Study:

  • To automatically and accurately classify news articles containing disinformation.
  • To identify linguistic and headline characteristics that differentiate disinformation from benign information.
  • To develop automated fact-checking capabilities for veracity assessment.

Main Methods:

  • Building classifiers to analyze linguistic clues and headline clickbaitiness in fake news.
  • Developing a natural language inference (NLI) module for automated disinformation verification.
  • Creating a new dataset annotated with veracity labels and supporting evidence based on fact-checking standards.

Main Results:

  • Disinformation exhibits features from both propaganda and mainstream news, complicating detection.
  • Automated fact-checking shows significant potential for incorporating veracity assessment.
  • Error analysis highlights challenges and identifies areas for future improvement in automated fact-checking.

Conclusions:

  • Automated systems can assist in classifying disinformation and verifying veracity.
  • The developed NLI module and dataset offer a foundation for advancing automated fact-checking.
  • A web app was implemented to aid manual fact-checkers by gathering and assessing evidence.