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Maximum entropy networks for large scale social network node analysis.

Bart De Clerck1,2, Luis E C Rocha1,3, Filip Van Utterbeeck2

  • 1Department of Economics, Ghent University, Ghent, Belgium.

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|October 4, 2022
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Summary
This summary is machine-generated.

Maximum entropy network models effectively identify statistically significant interactions in large social networks, aiding in the detection of disinformation campaigns. This approach enhances the identification of users involved in coordinated online influence operations.

Keywords:
Disinformation identificationMaximum entropy networksNetwork analysisSocial networks

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Area of Science:

  • Computational Social Science
  • Network Analysis
  • Information Operations

Background:

  • Disinformation campaigns pose a significant challenge in online environments.
  • Analyzing large-scale social network data is crucial for understanding these operations.
  • Existing computational techniques are being advanced for broader application.

Purpose of the Study:

  • To apply maximum entropy network models to identify statistically significant interactions within disinformation campaigns.
  • To evaluate the suitability of these models for non-supervised community detection in large social networks.
  • To assess the impact of data sampling on the reconstruction of disinformation operations.

Main Methods:

  • Utilized maximum entropy network models on Twitter datasets from information operations reports.
  • Constructed interaction networks and compared them against a null model to identify significant connections.
  • Validated the method for its effectiveness in unsupervised community detection.
  • Tested the robustness of the models against missing data.

Main Results:

  • The method successfully identified statistically significant user interactions in large social networks.
  • The prevalence of users involved in disinformation campaigns was higher when significant interactions were extracted.
  • Different network models offered varied insights and identified distinct patterns.
  • Accurate data sampling was found to be critical for reconstructing disinformation operations.

Conclusions:

  • Maximum entropy network models are suitable for analyzing large social networks and detecting disinformation campaigns.
  • The identification of statistically significant interactions is key to uncovering coordinated online activities.
  • Model selection influences the perception of data and the patterns identified.
  • Data integrity and sampling are paramount for accurate operational reconstruction.