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Tracking online topics over time: understanding dynamic hashtag communities.

Philipp Lorenz-Spreen1, Frederik Wolf2, Jonas Braun3

  • 11Institute of Theoretical Physics, Technische Universität Berlin, Hardenbergstraße 36, 10623 Berlin, Germany.

Computational Social Networks
|November 13, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces novel network science tools to analyze hashtag dynamics, revealing bursty usage patterns. A new model explains these topic popularity shifts, offering insights into online communication trends.

Keywords:
Aging modelBurstsHashtagsMemory matchingOnline mediaRandom walkRankingTemporal community detectionTopic dynamics

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Area of Science:

  • Network Science
  • Computational Social Science
  • Data Science

Background:

  • Hashtags are crucial for online topic characterization and communication.
  • Analyzing hashtag dynamics reveals bursty usage patterns in online media.
  • Existing methods lack robust tools for tracing time-dependent hashtag behavior.

Purpose of the Study:

  • To develop novel tools for analyzing hashtag dynamics in time-dependent data.
  • To model the bursty behavior of hashtag usage and topic popularity.
  • To understand the temporal evolution of online topics through hashtag analysis.

Main Methods:

  • Building temporal and weighted co-occurrence networks from hashtags.
  • Inferring community structure on static and temporal networks using customized methods.
  • Developing a novel stochastic model incorporating time-dependent prestige scores for dynamic rankings.

Main Results:

  • Hashtag usage exhibits bursty increases and decreases, characterized by fat-tailed distributions of gains, losses, and interevent times.
  • A novel dynamic ranking model successfully reproduces observed bursty hashtag dynamics.
  • The proposed methodology reveals robust temporal behavior of online topics and communities.

Conclusions:

  • Network science methods provide effective tools for analyzing complex hashtag dynamics.
  • The developed model offers a parsimonious explanation for topic popularity fluctuations in online media.
  • Understanding these dynamics is key to comprehending the evolution of online discourse.