EchoHunt — digital discourse monitoring

The problem

Narratives move fast across social media, and by the time a harmful or misleading story is obvious to a communications team, it has often already spread across multiple platforms and languages. Manually tracking thousands of posts across Twitter, Instagram, YouTube and Facebook — in multiple languages, including text baked into images and spoken in videos — is not something a human team can do at scale or in real time. Worse, once a narrative is spotted, teams typically start a response from scratch: figuring out who is driving it, how credible those sources are, which platforms need the most attention, and what to actually say, all under time pressure while the story keeps moving.

Approach

We built an end-to-end platform that takes a topic, hashtag or account and turns raw social media activity into a ranked map of the narratives forming around it, then drafts a response. Posts are collected across all four platforms and enriched so nothing is missed for being outside the caption or outside English: on-image and on-video text is extracted via OCR, spoken audio is transcribed, and everything is translated to a common language before analysis.

From there a multilingual NLP pipeline detects entities, sentiment and stance toward a chosen target, then clusters posts into distinct narrative storylines — weighted by credibility signals such as account verification and official-source status rather than raw engagement, so a narrative pushed by a handful of credible accounts is not buried under viral but low-credibility noise. Each cluster is scored for influence using a transparent, weighted framework, so an analyst can see exactly why a narrative ranked where it did. A three-agent AI crew — analyst, strategist and tactician — then reads the clusters and produces a platform-by-platform counter-narrative plan: what to say, in which language, on which platform, and how urgently. The pipeline runs on local GPU infrastructure by default, so sensitive monitoring data never has to leave the client's own environment, with cloud LLMs available as an opt-in, bring-your-own-key choice rather than a requirement.

Outcome

Communications and analyst teams get one workflow in place of hours of manual, multi-platform monitoring: scrape, enrich, detect, review, respond. Instead of discovering a narrative after it has already spread and then starting a response cold, they see narratives ranked by real credibility and influence signals and receive a drafted, platform-specific strategy they can act on immediately. The system handles the full multilingual, multi-platform picture — including content that never appears in searchable text — so a narrative cannot hide in an image, a video, or a language nobody was watching for.