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Interdisciplinary Lessons Learned While Researching Fake News.

Char Sample1, Michael J Jensen2, Keith Scott3

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Fake news, a long-standing issue, is increasingly recognized globally. This study proposes an interdisciplinary model to evaluate news veracity by analyzing context, spread patterns, and author/publication archives.

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Area of Science:

  • Digital Deception and Media Studies
  • Interdisciplinary analysis of misinformation and disinformation

Background:

  • Misleading and propagandistic news reporting has historical roots in American journalism.
  • The term "fake news" encompasses diverse phenomena, from satire to disagreeable content, gaining prominence in 2016.
  • Global recognition of fake news as a problem has spurred mitigation initiatives with limited success.

Purpose of the Study:

  • To explore the history and interdisciplinary nature of deception in news reporting.
  • To discuss how various academic disciplines contribute to detecting and countering fake news.
  • To introduce a novel evaluation model for assessing news veracity.

Main Methods:

  • Interdisciplinary review of deception history and its relation to fake news.
  • Analysis of different fake news types (e.g., altered photos, deepfakes) and detection technologies.
  • Introduction of a three-point evaluation model: contextual data, spread patterns, and archival analysis.

Main Results:

  • The interdisciplinary nature of digital deception complicates holistic solutions.
  • Existing technologies for identifying fake news have limitations.
  • The proposed model offers a framework for measuring a story's distance from factual accuracy.

Conclusions:

  • A comprehensive approach integrating insights from psychology, sociology, and other fields is crucial.
  • The proposed evaluation model provides a starting point for assessing news veracity, though it cannot definitively distinguish fact from fiction.
  • Further research can refine this model to improve the detection and countering of fake news narratives.