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Area of Science:

  • Social Sciences
  • Computational Social Science
  • Information Science

Background:

  • Social media platforms host diverse communities with divergent conversational trajectories.
  • Disinformation and manipulated messages can significantly influence public opinion.
  • Understanding inter-group disparities is key to analyzing information flow.

Purpose of the Study:

  • To quantify disparities between opposing social media communities.
  • To uncover distinct strategies used by these communities for campaign promotion.
  • To analyze the temporal dynamics of information dissemination in online groups.

Main Methods:

  • Utilized functional data analysis to examine temporal dynamics of social media groups.
  • Assessed group behavior using time-dependent metrics like posts and retweets.
  • Investigated Twitter data related to high-profile incidents (Skripal/Novichok, Bucha Crimes).

Main Results:

  • Preliminary findings highlight quantifiable differences in information dissemination strategies between communities.
  • Identified temporal patterns in post and retweet activity correlating with specific campaigns.
  • Revealed distinct behavioral mechanics employed by opposing online groups.

Conclusions:

  • The study offers new insights into the mechanics of information dissemination on social media.
  • Quantifying community disparities aids in understanding the spread of information and disinformation.
  • Findings can inform strategies for optimal response times to online campaigns.