Coordinated Inauthentic Behavior Detection
Coordinated inauthentic behavior campaigns manipulate public discourse at scale — and the tools to detect them are locked inside platforms that have no incentive to share what they find. Independent researchers, journalists, and analysts are left building cases with incomplete signals and borrowed intuition.
The SolutionA multi-layer behavioral detection pipeline that identifies CIB through temporal entropy, network coordination, and content propagation analysis — detecting how accounts behave, not what they say.
Every layer operates on a different behavioral signal. An account can be flagged by one layer and cleared by another — the synthesis resolves these into a unified assessment.
Measures how predictable each account's posting schedule is using Sample Entropy (SampEn). Automated accounts tend to post with mechanical regularity that humans rarely sustain. Low entropy signals behavioral rigidity.
Builds a similarity graph where edges connect accounts with Jensen-Shannon Divergence below threshold. The Leiden algorithm partitions this into communities of behaviorally similar accounts — coordination detected through collective pattern, not individual signals.
Identifies accounts sharing identical or near-identical content — URLs, hashtags, and text hashes. Cross-references with L2 communities to distinguish organic viral sharing from coordinated content amplification.
Applies large language model analysis to detect narrative coordination — thematic alignment, messaging discipline, and rhetorical patterns that suggest centralized direction rather than organic discourse.
Validated on the Internet Research Agency dataset released by Twitter — 9 million events from 3,836 accounts involved in a confirmed influence operation.
Study coordinated campaigns with a structured, reproducible pipeline that generates behavioral evidence — not just keyword matches or follower counts. Exportable artifacts for peer review and publication.
When a narrative goes viral, determine whether the amplification is organic or manufactured. BLOODHOUND surfaces the behavioral fingerprint behind coordinated campaigns before the story solidifies.
Supplement existing content-based moderation with behavioral signals. Detect coordination patterns that content analysis alone can't see — accounts that never post the same text but move in lockstep.
BLOODHOUND's detection engine is platform-agnostic. Adapters normalize platform-specific data formats into a common behavioral schema.
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