socialdrift
Temporal Auditing of Social Interaction Networks
Tools for constructing, auditing, and visualizing temporal social interaction networks from event-log data. Supports graph construction from raw user-to-user interaction logs, longitudinal tracking of network structure, community dynamics, user role trajectories, and concentration of engagement over time. Designed for computational social science, platform analytics, and digital community health monitoring. Includes four longitudinal audit indices: the Network Drift Index ('NDI'), Community Fragmentation Index ('CFI'), Visibility Concentration Index ('VCI'), and Role Mobility Index ('RMI'). 'NDI', 'CFI', 'VCI', and 'RMI' are purpose-built composite scores for longitudinal platform auditing.
socialdrift 
socialdrift is an R package for building, auditing, and visualising temporal social interaction networks from raw event-log data.
Most existing tools start after the graph already exists. socialdrift
takes you from a table of raw platform events (follows, replies, mentions,
likes) all the way through to longitudinal diagnostics, community health
reports, and inequality audits.
Installation
# Install from GitHub (development version)
# install.packages("remotes")
remotes::install_github("causalfragility-lab/socialdrift")
Workflow at a glance
library(socialdrift)
# 1. Standardise event data
ev <- as_social_events(sim_social_events,
actor_group = "actor_group",
target_group = "target_group")
# 2. Build monthly graph snapshots
gs <- build_graph_series(ev, window = "month")
# 3. Structural metrics
summarize_network_series(gs)
# 4. Network Drift Index
ndi <- network_drift(gs)
plot_network_drift(ndi)
# 5. Community dynamics
comm <- detect_communities_ts(gs)
community_fragmentation_index(comm)
# 6. User role trajectories
roles <- role_trajectories(gs)
plot_role_trajectories(roles)
# 7. Group disparity audit
audit_group_disparities(ev, gs)
Signature indices
| Index | Function | Description |
|---|---|---|
| NDI | network_drift() |
How much did overall network structure change? |
| CFI | community_fragmentation_index() |
Are communities becoming more siloed? |
| VCI | visibility_concentration_index() |
Is attention concentrating on fewer actors? |
| RMI | role_mobility_index() |
How often do users transition between roles? |
Structural user roles
Each node is assigned one of six interpretable roles per period:
| Role | Description |
|---|---|
isolated |
No connections in this period |
peripheral |
Low degree, not a bridge |
broadcaster |
High out-degree, low in-degree |
popular |
High in-degree, low out-degree |
core |
High in- and out-degree |
bridge |
High betweenness centrality |
Example use cases
- Online learning communities --- Who becomes isolated? Are discussions clique-based?
- Social media platforms --- Is recommendation driving unequal visibility?
- Workplace collaboration --- Are teams siloing after a reorg?
- Educational peer networks --- Do minority students become more peripheral over time?
Package modules
| Module | Key functions |
|---|---|
| Data engineering | as_social_events(), build_graph_series() |
| Structural metrics | network_density_ts(), reciprocity_ts(), clustering_ts(), degree_inequality_ts() |
| Community dynamics | detect_communities_ts(), community_drift(), community_fragmentation_index() |
| Role trajectories | classify_user_roles(), role_trajectories(), role_mobility_index() |
| Drift & inequality | network_drift(), visibility_concentration_index(), audit_group_disparities() |
| Plotting | plot_network_metrics(), plot_network_drift(), plot_role_trajectories() |
Citation
@Manual{socialdrift,
title = {socialdrift: Temporal Auditing of Social Interaction Networks},
author = {Subir Hait},
year = {2025},
note = {R package version 0.1.0},
}
License
GPL-3 (c) Subir Hait