Twitter Response to Munich July 2016 Attack: Network Analysis of Influence

Area of Science:

• Social media analysis
• Computational social science
• Network analysis

Background:

• Social media platforms facilitate communication for diverse actors.
• Analyzing online discourse is crucial for understanding public reaction and information spread during crises.
• The #Munich hashtag on Twitter became a focal point after a 2016 shopping mall shooting.

Purpose of the Study:

• To investigate the spheres of influence and community evolution on Twitter using the #Munich hashtag after an active shooter event.
• To understand how community dynamics affect information messaging campaign effectiveness.

Main Methods:

• Collected tweets containing the #Munich hashtag starting 1 hour after the event for approximately one month.
• Constructed daily and cumulative social networks from the collected Twitter data.
• Applied the Louvain algorithm to analyze community evolution and changes over time.

Main Results:

• Large, influential communities initially formed around the #Munich hashtag.
• These prominent communities diminished and disappeared from the #Munich conversation within approximately 7 days.
• A significant portion of the community activity appeared politically charged.

Conclusions:

• The rapid disappearance of early communities suggests a transient nature of online discourse following major events.
• The politically charged nature of communities indicates a migration of conversations to other related Twitter hashtags.
• Future research should focus on tracking community evolution across different topics and timeframes to better understand information diffusion.