AI qualitative analysis of social media risks

Detect early spikes in hostile narratives and track which messages convert to local press pickup. Prioritize monitoring of posts by high-reach accounts and the subsequent comment sentiment and geographic spread.

Build an evidence chain: original post → archival snapshot → contract/doc timestamp → on-the-ground incident reports. That chain supports requests to platforms, government briefings, and legal remedies.

Treat social-media disclosures as mixed-methods data: code for frames (e.g., 'traitor', 'spy', 'waste'), map co-occurrence with named entities, and compare engagement patterns across audiences to identify who is driving escalation.

Evidano scrapes posts, captures archival snapshots, and ingests FOIA documents and transcripts so your corpus is time-stamped and auditable.

Use AI-assisted codebooks to extract frames ('funding the enemy', 'rogue contract') and export hierarchical themes and representative quotes for reports and briefings.

Automatically align timestamps across channels, map amplification paths (e.g., Musk → mainstream press → local outlets), and visualize co-occurrence networks to surface key amplifiers.

Compare reactions by geography, language, or follower cohorts to identify which audiences are most likely to translate online claims into offline harm.

Data is encrypted and never used to train third-party models. Use PII redaction, custom dictionaries for sensitive terms, and export ready-made evidence bundles for legal or policy use.

Ask an analyst-style AI to summarize the corpus, produce an executive timeline, or draft a briefing memo that cites exact quotes and document sources (exportable).

Import the X post and capture an archival snapshot; upload FOIA documents, contract PDFs and any related news URLs.

Run transcription/translation on interviews or foreign-language press; apply a custom dictionary for named entities (Halimi, USIP, DOGE).

Seed themes (e.g., 'allegation', 'amplifier', 'threat') and let Evidano propose subcodes; review & lock the codebook.

Generate theme counts, representative quotes, and cross‑tabulate by date and outlet to see how the narrative evolved.

Create a timeline visualization and a co‑occurrence network to identify key nodes and the lag between post and reported harm.

Export a PDF briefing with time-stamped artifacts, raw quotes, and redacted PII for sharing with legal or security teams.

Set automated scraping and alert rules for new mentions, sudden engagement spikes, or re-emergence of harmful frames.