Misinformation in India spreads through WhatsApp forwards, coordinated bot networks, and AI-generated images — in Hindi, Hinglish, and English. ShadowTrace hunts all three, in real time.
By the time a claim is manually debunked, it has already reached millions of family group chats. Detection has to happen at the speed of the campaign — not the newsroom.
Every agent runs real inference on input you provide — no canned results, no staged datasets. Type an account, paste a claim, drop an image URL.
Groq Llama-3.3-70B scores any claim for misinformation, blended with forward-chain pattern detection built for Hindi, Hinglish, and English.
Error Level Analysis heatmaps plus a three-model classifier ensemble. An image is only called AI-generated when two models independently agree.
Enter any real account. Posts are ingested live, then analysed for 60-second posting sync, stylometric fingerprints, and LLM-generation signals.
Neo4j-backed campaign topology. Origin nodes, bot clusters, and amplifier chains rendered as an explorable force-directed graph.
Sarvam AI identifies code-mixed and regional text — because misinformation in India does not arrive in English.
A LangGraph pipeline chains every signal into one verdict: organic misinformation, coordinated inauthentic behaviour, or state-level operation.
Paste a WhatsApp forward and watch the chain execute — each agent reporting its own finding, with real latency, ending in an LLM-written threat assessment.
The claim asserts a home remedy cures a viral infection — false, and dangerous if it displaces medical treatment. Written by the model at inference time.
Paste any claim. Type any real account. Drop any image URL. The dashboard is live — every score you see is computed the moment you ask for it.
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