Data: Social Media Pattern Analysis

Summary: Pattern analysis synthesizes platform transparency datasets with academic infrastructural context [src:source-studies-indian-politics-2022].

Data Inputs:

• Political ad spend/time distribution [src:source-facebook-transparency-2023].
• Content moderation/policy enforcement metrics [src:source-twitter-transparency-2023].
• Publicly observable posting frequency clusters (media reported) [src:source-thehindu-digital-2020].

Processing:

• Time binning (daily).
• Rolling 7-day normalization for spike detection.

Indicators:

• Spend concentration ratio.
• Hashtag burst intervals (observational).
• Cross-platform theme persistence index.

Limitations: API/raw logs not fully accessible; reliance on published transparency aggregates.

Independent Verification Path:

1. Retrieve platform transparency reports.
2. Replicate time bin and rolling average calculations.
3. Compare detected spikes with reported event dates.

References: Listed above.