Built on a decade of Telegram infrastructure. NarraPrism is the analytical layer above an indexing system that has continuously processed public Telegram channels since 2015.
Discourse grouped into evaluative claims — "Trump is the only one who can end the war" — not topic clusters or keyword counts. A claim that lives across many phrasings is one object.
The same narrative tracked in Russian, English, Farsi, Arabic, German, Hebrew, Ukrainian and Chinese — one claim, eight expressions.
What channels publish and how their audiences actually react are tracked separately. The gap between them is often the finding.
Communities are detected from who reposts whom, not assigned manually. Surfaces who is amplifying which narrative — empirically, not by guess.
NarraPrism is the third layer in a continuous research stack. The directory and search products generate the indexing pipeline and operational dataset that the analytical layer is built on.
Structured public directory organized by topic, country and language. 132,000+ communities published across 271 categories, 72 languages, 154 countries. The original indexing system, continuously maintained.
Dedicated search across tens of millions of public Telegram communities, with structured filters for activity, language, country, member count. The retrieval layer for researchers, journalists, OSINT practitioners and NGOs.
Analytical layer for content intelligence and large-scale narrative analysis. Built on the corpus, repost graph and channel metadata accumulated over the previous decade. Currently scoped to eight political languages.
The full indexed corpus by language. The eight-language deep-analysis layer (see Findings below) sits on top of this base.
The deep-analysis layer runs on a focused corpus extracted from the wider index. Findings below are produced from this layer.
From a 3.4M-post analytical corpus covering Trump, Putin and Zelensky across eight language ecosystems. Each finding is grounded in a stated method and explicit limitations.
Trump's public framing emphasizes ending wars. His audience's behavior shows the opposite preference. Anger on peace content registers near zero — they don't reject peace, they simply don't engage. War content drives the activity.
German-language channels apply both frames to Zelensky simultaneously: corruption critic and defender of Europe. Russian channels never use "defender." This is a German position — but Russian information infrastructure (Alina Lipp, InfoDefenseDEUTSCH) amplifies the corruption side specifically.
English MAGA, Russian imperialist channels, and Ukrainian domestic opposition (around Poroshenko's faction) all push anti-Zelensky framing — through different sources, in different languages, for different goals. Convergence without coordination is the harder pattern to see and the more interesting one.
Communities are detected from who-reposts-whom on a 6,761-node, 9,696-edge graph. No manual labels in the detection step. Names assigned post-hoc by reading the channels in each cluster.
Count = channels and source-nodes assigned to the community. Cross-reference: 355 narrative-to-community links computed with weight = share of narrative posts originating in the community.
How anti-Zelensky narratives converge without coordination across eight languages — and why a single-foreign-actor frame misses two-thirds of what's actually happening. Analysis of 3.4M posts across three leaders, eight language corpora, channel stance segmentation, audience reactions, and repost network structure.
What a channel publishes and how its audience reacts are two different opinions. NarraPrism separates them by default — and the gap is frequently where the analytically interesting result lives.
A single claim tracked across eight political languages. Convergence between unrelated infrastructures (no shared sources, similar messaging) is detectable because the analysis sits above language.
Communities are detected from amplification structure, not assigned by hand. Surfaces who is reposting whom — and which narrative is being amplified by which empirical cluster of channels.
Each narrative is linked to the communities amplifying it, with weights. Lets the analyst answer: "this claim — which network is carrying it, and how strongly?"
NarraPrism ingests content from public Telegram channels. The deep-analysis corpus currently spans eight languages (English, Russian, Ukrainian, German, Farsi, Arabic, Hebrew, Chinese), drawn from a continuously updated index of channels meeting activity and size criteria.
Narratives are extracted as evaluative claims and deduplicated into canonical objects across languages and channel camps. Channel stance and audience reaction are treated as separate analytical layers — the gap between them is, in practice, where the interesting result tends to sit. Audience signal is derived from platform engagement, normalized to within-language baselines, not from generated emotional labels.
Communities are detected from the repost graph via graph-based community detection — no manual labels in the detection step. Each narrative carries weighted attribution to the communities amplifying it.
Every report states confidence bounds and known failure modes. Detailed methodology, validation procedures and reproducible query sets are provided to institutional partners under NDA.
How publics in target language ecosystems are receiving a decision — channel framing vs audience reaction, before mainstream coverage catches up.
Cross-language narrative briefs on contested topics, with traceable method and reproducible queries — citable in publications.
Detection of narrative environments that affect mission areas (migration, conflict, human rights) — empirical, not anecdotal.
Repost-graph traces from a story back to source communities. Convergence patterns between unrelated networks.
An international team of Telegram researchers and data engineers — a decade of continuous work in this space.
The team began operating large-scale messaging communities before Telegram existed. When Telegram introduced supergroups and a proper moderation architecture in 2015, that early operational involvement produced the first structured dataset on the platform — maintained and extended ever since.
Over a decade, the collection infrastructure has processed a significant portion of the Telegram ecosystem. The majority is filtered at the collection layer — inactive accounts, short-lived groups, communities below threshold. What is retained is the operational corpus on which NarraPrism's analytical layer runs.
Verification, team credentials and references are provided to institutional partners under NDA on request.
NarraPrism is currently in private access. We prioritize institutional partners — research organizations, policy units, NGOs and government clients with a defined use case. Custom analysis and sample reports are available on request.
Direct contact: research@narraprism.com