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Classification of unlabeled online media.

Sakthi Kumar Arul Prakash1, Conrad Tucker2,3,4,5,6

  • 1Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213-3890, USA.

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|March 26, 2021
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Summary

This study introduces an unsupervised method to detect fake news online by analyzing user interactions, not content. It uses network entropy to differentiate authentic from misinformation without needing labeled data.

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Area of Science:

  • Social Network Analysis
  • Information Science
  • Computational Social Science

Background:

  • Misinformation detection typically requires labeled datasets, limiting scalability.
  • Existing methods often rely on content analysis, which can be circumvented.
  • Understanding information spread dynamics in social networks is crucial.

Purpose of the Study:

  • To develop an unsupervised method for classifying misinformation in social media.
  • To leverage user-user and user-media interactions for fake news detection.
  • To model information propagation using network topology and entropy.

Main Methods:

  • Created an experimental social media platform to simulate information spread.
  • Developed a graphical model to analyze network topology evolution.
  • Modeled uncertainty (entropy) propagation for fake and authentic media.
  • Utilized user-user and user-media interaction data (e.g., likes).

Main Results:

  • User-user and user-media interaction entropy approximates fake and authentic media likes.
  • Demonstrated the ability to classify fake media in an unsupervised manner.
  • Established a scalable approach to misinformation classification.

Conclusions:

  • Network interaction patterns can effectively distinguish between fake and authentic information.
  • Unsupervised learning based on network dynamics offers a promising alternative for misinformation detection.
  • This approach bypasses the need for content analysis and ground truth labels.