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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Published on: January 18, 2020

Quantifying collective attention from tweet stream.

Kazutoshi Sasahara1, Yoshito Hirata, Masashi Toyoda

  • 1Graduate School of Information Science, Nagoya University, Nagoya, Japan. sasahara@is.nagoya-u.ac.jp

Plos One
|May 3, 2013
PubMed
Summary
This summary is machine-generated.

Researchers developed a method to detect collective attention on Twitter by analyzing tweet activity bursts. This technique quantifies public focus on events, offering insights into digital-era social behavior.

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Area of Science:

  • Computational Social Science
  • Network Science
  • Digital Humanities

Background:

  • Online social media generates vast amounts of behavioral data, offering opportunities to study human interactions.
  • Analyzing large-scale, time-variant social media data presents significant challenges.

Purpose of the Study:

  • To develop a method for detecting and measuring collective attention on Twitter.
  • To quantify the intensity of public focus on real-world events using social media data.

Main Methods:

  • Analyzed tweet activity patterns, identifying bursts and oscillations indicative of collective attention.
  • Utilized Jensen-Shannon divergence to measure the intensity of deviations from regular tweeting rhythms.
  • Linked detected attention spikes to specific real-world events based on tweet popularity and keyword enhancement.

Main Results:

  • Successfully identified 60 instances of collective attention from a dataset of 490 million Japanese tweets.
  • Demonstrated the method's effectiveness in capturing public focus, including attention surrounding the Tohoku-oki earthquake.
  • Investigated retweet networks to understand the social interaction dynamics of collective attention.

Conclusions:

  • The proposed method offers a simple yet effective way to retrospectively summarize collective attention on social media.
  • This approach contributes to a fundamental understanding of social behavior patterns in the digital age.
  • The findings highlight the utility of social media data for studying emergent social phenomena.