A one-day hackathon on disinformation, artificial intelligence, and the law. Build something that pushes back.
Each brief is deliberately broad. Prototype something playable, deployable, or demonstrable by the end of the day. Scope ruthlessly.
Build a gamified, AI-driven tool that trains users to recognise different flavours of disinformation — loaded language, false dichotomy, manufactured consensus, cherry-picking, whataboutism. Think Duolingo for critical thinking. Points optional, dopamine mandatory.
Generic deepfake detection is a moving target. A single-identity detector is tractable in a day. Zelensky is one of the most deepfaked world leaders of the decade — pick a checkpoint, fine-tune, ship a demo that eats a video and returns a verdict.
Pluggable into any channel as a slash command. Reads incoming messages, flags recurring propaganda narratives, shows receipts. Bonus: cluster narratives over time and map which channels are singing the same song.
Given a piece of suspected disinformation, find the earliest source. No clean dataset exists — this is the messy, open-ended one. Embed, search, scrape, reason. Creativity weighs more than accuracy here.
Curated starting points for each problem. Use them as training data, benchmarks, or inspiration. Pick one, don't try to use all of them.
12,836 short political statements from PolitiFact, each labelled across six degrees of truthfulness, with speaker, context, and history metadata. A natural deck of cards for a classification game.
Multi-dimensional repository combining news articles, social engagement signals, and spatiotemporal context from PolitiFact and GossipCop. Useful if the game wants to show how a lie travels, not just whether it's a lie.
4,000 manipulated videos across four generation methods (DeepFakes, Face2Face, FaceSwap, NeuralTextures) plus 1,000 real YouTube baselines. The standard pretraining set for facial forgery detection.
590 original YouTube videos of public figures and 5,639 matched high-quality deepfakes across varied ages, ethnicities, and genders. Closest public analog to the single-public-figure detection setup.
~16,000 pro-Kremlin disinformation cases with disproofs, propagating outlets, target countries, and full text — the single most useful labelled corpus for this hackathon. Scrapable directly; a Mendeley dump is also available.
~500 million tweets from Feb 2022 to Jan 2023, released by Chen & Ferrara for research on wartime information warfare. Many tweets link out to Telegram channels — a useful bootstrap for channel lists.
Which is the point. Tracing a fake story to its origin is an open research problem. We don't want a solved challenge — we want a creative attempt.
No labelled ground truth. No leaderboard. Just the open web, a suspicious claim, and you.
The gold-standard, community-maintained list of tools for open-source investigation — reverse image search, archive diving, geolocation, metadata forensics, social graph mining. Not a dataset. An arsenal. Treat it as the starting inspiration for Problem 04 and build your own pipeline on top.
Embedding-based near-duplicate detection across scraped news archives. Google Fact Check Tools API as a secondary signal. Wayback Machine for temporal ordering. LLM-driven narrative paraphrase detection. Reverse image search on accompanying media. Pick one angle and execute it deeply.
● Registration opens soon
One day · Four problems · Pizza included