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Gilad Lotan @Buzzfeed
- 1. 1 Impactful data science and machine learning at BuzzFeed BUZZFEED Gilad Lotan | @gilgul gilad.lotan@buzzfeed.com
- 3. - 600 items per day - 3.5M so far - 10 platforms - Top channels on Snapchat discover and YouTube - Over 300M subscribers - Data collection pipeline Truly cross platform
- 4. The heart of our culture - data feedback loops Humans Data Tools Reporting Tools On/Off-site Distribution Personalization Recommender Systems A/B Testing (content, product) Opportunity Sizing and Forecasting User Segmentation Data investigations and reporting Translation and Adaptation Recommendations
- 5. 1) We’re obsessed with giving every piece of content a fair opportunity to reach the right audience.
- 6. Humans learning from dashboards We invested heavily in tooling that surfaces performance data to our content creators, so they can then interpret that data and learn what’s engaging our audience (or what’s not)
- 7. Humans learning from dashboards
- 8. Translation Recommendations (using heuristics & logistic regression) Humans learning from recommendations Model by Lyle Smith, Ailin Fang
- 9. 17 of the Best... All the Reasons... 17 Reasons Why A winner is chosen! (much faster) Editors choose options Variants are tested for CTR Editors learn from what thumbs are chosen and adapt future thumbs Model by Lucy Wang Humans learning from A/B tests
- 10. FeedRanker Internal service which leverages machine learning to rank & feature our most engaging content Image borrowed from Adam Lofting, Model by Lucy Wang Uses a multi-armed bandit algorithm, which is a common ML method Machines that learn from human behavior
- 11. Recipe2Vec Internal system that surfaces similar recipes by learning vectorized representations for words in a corpus of recipes Model by Meghan Heintz Machines that learn content association
- 13. recipes with similar preparations but different flavor profiles… (smoothies v. boozy bevvies)
- 14. Neural Networks to make publishing decisions Promotion recommendations Internal system which leverages a neural network to predict the Facebook page that an unpublished item should be posted to Model by Andrew Metersky & Lucy Wang
- 15. V1: Bag of words classifier Look At This Adorable Dog Who Looks Like A Bear 16 Cows That Are Too Friggin’ Cute For Words Chili Dog Bread Ring The hot dog problem
- 16. Bidirectional LSTM Network chili dog bread ring LST M LST M LST M LST M LST M LST M LST M LST M 20 % Relevance to Animals Account chili dog != animal dog bread != animal
- 18. 2) Our audience inhabits a complex, polluted information environment. Our role is to help them navigate it, not pretend it doesn’t exist.
- 19. "I started the site for an easy way to make money," said a 17-year-old who runs a site with four other people… “Here in Macedonia the revenue from a small site is enough to afford many things.” …The more people who click through from Facebook, the more money they earn from ads on their website. BuzzFeed News first covered #FakeNews beat
- 20. 20 This is what fake followers look like
- 21. Mohd posted 71 comments to the same facebook post Is he real?
- 22. Facebook comment manipulation (cyborgs) Possible to approximate with a simple function
- 23. Jennifer commented 71 times on Tasty videos in the past month with a single word: ‘yummy’ Is she real?
- 24. Fader Networks: Manipulating Images by Sliding Attributes - NIPS 2017 https://github.com/facebookresearch/FaderNetworks We are being manipulated https://www.youtube.com/watch?v=ohmajJTcpNk Face2Face: real-time face capture and reenactment