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Concept drift detection on social network data using cross-recurrence quantification analysis.

Rodrigo F de Mello1, Ricardo A Rios2, Paulo A Pagliosa3

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This study introduces a method to detect concept drifts in social media data using Cross-Recurrence Quantification Analysis. The approach effectively identifies significant shifts in information and sentiment, correlating with major real-world social events.

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Area of Science:

  • Social Network Analysis
  • Data Science
  • Computational Social Science

Background:

  • Social network systems generate vast amounts of time-series data, including information content and user sentiment.
  • Detecting changes in these data generation processes, known as concept drifts, is crucial for understanding evolving social dynamics.

Purpose of the Study:

  • To propose and evaluate a novel approach for detecting concept drifts in time series data from social networks.
  • To identify significant textual changes in terms of information volume and sentiment polarity.

Main Methods:

  • Utilized Cross-Recurrence Quantification Analysis (CRQA) to analyze time series data.
  • Applied the method to data from the TSViz project, focusing on tweet information content and sentiment.
  • Evaluated the approach's ability to detect changes in data generation processes.

Main Results:

  • The proposed concept drift detection approach successfully identified changes in information and sentiment over time.
  • Detected concept drifts correlated with significant social events, validated by external news sources.

Conclusions:

  • Cross-Recurrence Quantification Analysis is effective for detecting concept drifts in social media time series.
  • This method provides a valuable tool for identifying key social events and shifts in online discourse.