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Generalized durative event detection on social media.

Yihong Zhang1, Masumi Shirakawa1, Takahiro Hara1

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Summary
This summary is machine-generated.

This study introduces a general method for detecting unusual events in social media data. The approach effectively identifies significant temporal phenomena, improving real-world applications like stock market prediction.

Keywords:
Event detectionHeuristic methodsSocial media

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Area of Science:

  • Natural Language Processing
  • Data Mining
  • Computational Social Science

Background:

  • Social media generates vast amounts of data, making event detection crucial.
  • Existing event detection methods often rely on specific event types or variable behaviors.
  • A general approach for social media event detection is needed.

Purpose of the Study:

  • To propose a general method for detecting durative events in social media discussions.
  • To develop an algorithm that makes minimal assumptions about event types or variable behavior.
  • To enable real-time event detection through an incremental algorithm version.

Main Methods:

  • Generalizing time unit representation using word embeddings from social media text.
  • Developing an algorithm to detect sustained deviations in semantic aspects indicative of events.
  • Implementing an incremental version for real-time event detection.
  • Evaluating the method on synthetic and two real-world datasets.

Main Results:

  • The proposed method effectively captures unusual events in social media discussions.
  • The retrospective and incremental algorithms show comparable performance on synthetic data.
  • The method demonstrates superior performance in capturing real-world news events compared to baselines.
  • Event detection using this approach significantly improves stock market movement prediction accuracy.

Conclusions:

  • The developed method offers a general and effective approach to social media event detection.
  • The algorithm's ability to identify unusual semantic behavior is key to its success.
  • Real-time detection is feasible with the incremental version.
  • Social media event detection can provide valuable insights for financial market prediction.