At BuzzFeed, it’s our mission to keep our social media audiences maximally engaged, by leveraging the hundreds of original articles, videos, and graphics we produce daily. With our vast network of social media distribution channels, we have a team of human curators who handpick the content that is most relevant and interesting to each channel’s audience. Of course, as our content production scales, curation becomes increasingly difficult — more and more content slips through the cracks and don’t reach their full potential audience. To this end, we’ve built a curation service, powered by natural language processing and deep learning techniques. It learns to: 1. Find the most relevant content for each channel 2. Identify and resurface content with evergreen appeal 3. Determine the optimal times in the day to post each content I’ll discuss the implications of this use case, how we developed our methods, and how the service cohabitates with existing human curation.

1. Lucy X. Wang, BuzzFeed
Matchmaking Audiences
to Content
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2. BuzzFeed
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4. A vast network of social channels
• 10+ platforms
• 400+ accounts
• 1200+ pieces of
content published a
week
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5. But how do we scale our curation?
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6. By mimicking human curation
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7. 3 use cases for AI
1. Surface relevant content for each channel
2. Surface evergreen content
3. Automate publishing
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8. Case 1: How do we recommend
relevant content for each channel?
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9. Content too often slips through the
cracks
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10. Training on human curation data
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11. A language-based relevance model
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12. Predicting relevance from titles
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13. Consolidated into one model
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14. Examples
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15. Impact
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unlike rate remains stable

16. Case 2: How do we find evergreen
content to re-circulate?
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17. Taking advantage of evergreen posts
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2016
55K clicks
2018
36K clicks

18. A model based on content and traffic
performance
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19. Not much human curation data
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20. timely
evergreen
Our labelling proxy
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21. timely?
Handling poorly-labeled data
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22. Modeling with uncertainty
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23. Examples
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24. Impact
median clicks per post doubled
(+108%) with addition of
evergreen recommendations
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unlike rate remains stable

25. Case 3: How do we automate
publishing?
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26. Mixing human and AI curation
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27. Optimal scheduling model
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28. Trying all scheduling times
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29. How do we validate these models?
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30. Limits of AI validation
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31. The loop isn’t complete without
humans
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32. Gathering user feedback
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33. Adapting to feedback
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34. Human decisions will always be key
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35. 35#AI8SAIS
Human decisions will always be key
19M views
800,000
simultaneous
viewers
33M views

36. Thank you!
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