Twitter Follower Dynamics for Attention Broker Analysis

Replication data from a study on social network evolution analyzes follower counts and timings for retweeted accounts and attention brokers. The dataset supports causal analysis of how content amplification drives triad transitivity in directed social networks. It contains hashed user identifiers for privacy protection.

Use Cases

• Analyze time-bounded follower counts to measure the impact of attention brokers on network structure.
• Apply difference-in-differences methods using follower timing data to establish causal links between amplification and triad transitivity.
• Study the evolution of directed social networks by examining follower events for retweeted accounts and their associated brokers.
• Identify patterns of attention brokerage by comparing follower dynamics between brokers' followers and non-followers.

Strengths

• Data originates from a published academic study on emergent social media structures, providing a research-grade foundation.
• User privacy is protected through non-reversible hashing of all usernames and follower IDs.

Limitations

• The dataset's size, row count, and specific column structure are not provided, limiting pre-analysis assessment.
• Data is derived from a specific platform (Twitter/X), which may limit generalizability to other social networks.
• The temporal coverage and geographic scope of the underlying social media data are unspecified.

Provenance

Source

ICPSR Harvested Dataverse

Collection Method

Data was gathered using a novel technique to identify time-bounded Twitter/X following events.

Freshness

The record was last updated in February 2026, indicating recent maintenance.