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Entropy-Based Approach for the Detection of Changes in Arabic Newspapers' Content.

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This study introduces a novel method to detect social change by analyzing linguistic shifts in Arabic newspapers. The approach uses word embeddings to identify changes in newspaper content, proving reliable during the Arab Spring.

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Area of Science:

  • Computational Linguistics
  • Social Science Research
  • Digital Humanities

Background:

  • Social and political transformations are often reflected in media content.
  • Analyzing linguistic shifts in newspapers can provide insights into societal changes.
  • Existing methods may not capture real-time or nuanced changes effectively.

Purpose of the Study:

  • To propose a novel, online method for recognizing significant changes in social states.
  • To leverage transformations in the linguistic content of Arabic newspapers as indicators of social change.
  • To develop a reliable system for detecting shifts in newspaper content during critical periods.

Main Methods:

  • Utilizes pre-trained vector representations of Arabic words (word embeddings).
  • Employs a two-step procedure involving similarity distribution comparison and entropy calculation.
  • Incorporates a repeating under-sampling approach with a two-sample test for robust change point detection.

Main Results:

  • The method successfully identifies changes in linguistic templates within Arabic newspaper content.
  • Numerical experiments on newspapers from the Arab Spring period demonstrate high reliability.
  • The detected alterations in linguistic material serve as effective indicators of social state changes.

Conclusions:

  • The proposed method offers a reliable approach for online detection of social change through linguistic analysis of newspapers.
  • This technique provides a valuable tool for researchers studying social dynamics and media transformations.
  • The findings highlight the potential of computational linguistics in understanding socio-political shifts.