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Area of Science:

  • Social network analysis
  • Computational social science
  • Political communication

Background:

  • Public opinion is increasingly shaped by online discourse.
  • Twitter's network structure (retweets, replies) influences information diffusion.
  • Understanding online debate dynamics is crucial for accurate public opinion assessment.

Purpose of the Study:

  • To analyze public debate on Twitter using network representations.
  • To identify how direct and mediated tweet effects shape public opinion perception.
  • To detect potentially misleading representations of public opinion on the platform.

Main Methods:

  • Network analysis of retweets and replies on Twitter.
  • Case studies of the Saxon state elections and Leipzig riots (2019).
  • Quantification of user activity and confrontational behavior within debate networks.

Main Results:

  • Different opinion groups have unequal impact on public opinion due to varying engagement levels.
  • Users retweeting far-right content exhibit significantly higher activity and visibility.
  • These users engage more confrontationally, primarily replying to opposing groups.

Conclusions:

  • Online debate networks can create disproportionate visibility for certain viewpoints.
  • The structure of online interactions can lead to a skewed perception of public opinion.
  • Further research is needed to understand and mitigate biases in digital public spheres.