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Detecting global events is possible by analyzing communication network structures. Our method identifies viral information cascades crossing community boundaries, even without a significant increase in overall communication volume.

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Area of Science:

  • Network Science
  • Information Diffusion
  • Computational Social Science

Background:

  • Viral information spreading exhibits distinct patterns related to community structure.
  • Global events are hypothesized to trigger information cascades that transcend community boundaries.

Purpose of the Study:

  • To propose and validate a method for detecting large-scale events using temporal communication network structures.
  • To demonstrate that event detection is feasible by monitoring intra- and inter-community communication patterns.

Main Methods:

  • Analyzing temporal communication networks (email and Twitter).
  • Comparing communication volume within and across network communities.
  • Identifying information cascades that cross community boundaries.

Main Results:

  • The proposed method successfully detected events by analyzing communication network structures.
  • Event detection was effective even when overall communication volume did not significantly increase.
  • The approach was validated using the Enron email network and Twitter data during the Boston Marathon bombing.

Conclusions:

  • Monitoring communication network structures offers a robust method for detecting large events.
  • The diffusion patterns of viral information, particularly across community boundaries, are key indicators of significant events.
  • This approach provides a novel way to identify emergent events in online and offline communication systems.