At BuzzFeed, it’s our mission to share the best content of the internet with the world through our 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 and our social networks grow, curation becomes increasingly difficult. To this end, we’ve built a curation workflow 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 and 3. Determine the optimal times in the day to post each content. In this talk we will show real-world examples of how neural networks helped boost our performance. We plan to discuss the implications of each use case, how we developed our methods, and how the service cohabitates with existing human curation in our internal workflow tools.
Lucy X Wang is a Senior Data Scientist at BuzzFeed working on machine learning tools for optimizing audience reach and engagement. She performed research on social networks and information diffusion at Columbia University.
Swara Kantaria is a Senior Product Manager at BuzzFeed, a media company that publishes all the trending content people will want to share. Since joining the company, she’s worked on a range of products from launching buzzfeed.com in international market to building new internal tools. Most recently, Swara is the Product Lead for Distribution Tools, a team that team is responsible for building products that send all of our content to social media platforms (like Facebook, YouTube, Twitter and Instagram), OTT destinations (like Roku and Pluto) and to syndication partners. Prior to joining BuzzFeed, Swara was a technology consultant at Deloitte.
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