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An Innovative Way to Model Twitter Topic-Driven Interactions Using Multiplex Networks.

Obaida Hanteer1, Luca Rossi1

  • 1Data Science and Society Lab, IT University of Copenhagen, Copenhagen, Denmark.

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|March 11, 2021
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Summary
This summary is machine-generated.

We introduce a new model for analyzing implicit user interactions on Twitter by grouping hashtags into topics. This thematic multiplex network approach reveals novel communities missed by traditional methods.

Keywords:
multiplex community detectionmultiplex networkssocial media data analysissocial network analysisthematic clustersthematic communitiesthematic multiplex

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Area of Science:

  • Social Network Analysis
  • Computational Social Science
  • Information Science

Background:

  • Traditional models often overlook nuanced user interactions on social media platforms like Twitter.
  • Implicit interactions, inferred from shared content, are crucial for understanding online communities.
  • Hashtag usage provides a rich source of data for topic-based social network analysis.

Purpose of the Study:

  • To develop a novel method for modeling topic-based implicit interactions among Twitter users.
  • To construct a thematic multiplex network representing user connections based on shared hashtags within topics.
  • To demonstrate the effectiveness of this model in uncovering previously unobserved community structures.

Main Methods:

  • Grouping Twitter hashtags into distinct themes or topics within a specific context.
  • Constructing a thematic multiplex network where each layer represents a topic.
  • Users are connected within a layer if they share the same hashtag for that topic.
  • Applying multiplex community detection algorithms to the constructed network.

Main Results:

  • The proposed model successfully represents topic-based user interactions on Twitter.
  • Multiplex community detection on the thematic multiplex identified new community structures.
  • These newly found communities were not detectable using traditional Twitter interaction modeling techniques.

Conclusions:

  • The thematic multiplex network model offers a more comprehensive approach to understanding Twitter user interactions.
  • This method enhances the discovery of complex community structures by incorporating topic-specific information.
  • The findings suggest broader applications in social media analysis and understanding online group dynamics.