Geetu Ambwani, Principal Data Scientist, Huffington Post at MLconf NYC - 4/15/16

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MLconf

Data Science in the Newsroom

Technology

What is the Huffington Post?
Founded May 2005
Ranking among Digital-only news websites 1
Cross-platform monthly unique visitors Over 187 Million
Number of articles per day Over 500
Number of international editions 15
Bloggers Over 100,000

News Industry - Trends
HuffPost has consistently been an innovator in the digital publishing space.
Massive Blogging Network:
More than 100K bloggers across the globe

News Industry - Trends
HuffPost has consistently been an innovator in the digital publishing space.
Google Site Rank

News Industry - Trends
HuffPost has consistently been an innovator in the digital publishing space.
Biggest Social publisher

Ad campaigns
International editionsSocial media promotion
Editors
User-experience
Blog moderators
Reporters
HuffPost Studio

Content Lifecycle
DistributionCreation Consumption

Content Creation: How Can Data Help ?
● Tools to help surface, discover trends in different parts of the web
● Content Enhancement with multimedia based on semantic matching (images, slideshows, videos)
● Optimizing headlines/images (RobinHood Platform)

Content Gap: Production Versus Consumption

Content Consumption: How Can Data Help?
Know Your Audience
● User Cohorts:
○ Social Traffic versus FrontPage Clickers consume different content
○ Desktop Vs Mobile consumption
● Recommendations/Personalization
● Can we use data to inform product design and interface ?
○ Rearrange share buttons based on traffic origin (Facebook vs Pinterest)

Content Distribution: Can Data Help ?
● People’s attention is increasingly concentrated on social streams
○ More traffic to publishers from social than any other way
● Are Distributed Platforms the new home page ?
○ Facebook Instant, Apple News, Snapchat Discover, Google Amp
○ Messenger Bots
● You need to be where your audience is:
○ Identify the content mix that is maximally engaging on an external platform
○ Can we use data to seed these distribution networks ? (Facebook HuffPost Pages, Snapchat
Discover)

Content Distribution: Can Data Help ?
● HuffPost produces 1000 articles a day - which of these do we promote ?
● Article PVs follow a very skewed distribution of success
○ Only 1% of our articles > 100k PVs
● Content performs differently on different networks.
● Can we predict the articles that will get traction in advance so
■ We can optimally seed multiple distribution channels (Facebook HP Pages, Snapchat
Discover)
■ Target for premium/high value ads to maximize revenue
■ Populate Recommendation Widgets

Content Distribution: Can Data Help ?
Challenges
● Histogram of traffic distribution - highly skewed.
● The very act of promoting something causes a bump in traffic.
● Data normalization - how long do want to wait before predicting ?
● Very imbalanced data set
Our Approach
● Random Forest classifier.
● Multiple success criteria
● Historical examples of (+) and (-) articles. Downsampling.
● Different normalization thresholds
● Feature engineering: traffic growth ratios; initial organic social traffic per minute; distinct referrers;

Slackbot for the social promotion team
● 20% lift in PVs per predicted article

Conclusion
A Data Driven Newsroom today means
● More than just keeping track of clicks and shares
● Using predictive analytics to drive product and content placement
Machine Learning will be a key driver for success with the advent of distributed
content

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Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

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ICT role in 21st century education and its challenges

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Geetu Ambwani, Principal Data Scientist, Huffington Post at MLconf NYC - 4/15/16

1. Data Science in the Newsroom Geetu Ambwani Principal Data Scientist geetu.ambwani@huffingtonpost.com

2. What is the Huffington Post? Founded May 2005 Ranking among Digital-only news websites 1 Cross-platform monthly unique visitors Over 187 Million Number of articles per day Over 500 Number of international editions 15 Bloggers Over 100,000

3. News Industry - Trends HuffPost has consistently been an innovator in the digital publishing space. Massive Blogging Network: More than 100K bloggers across the globe

4. News Industry - Trends HuffPost has consistently been an innovator in the digital publishing space. Google Site Rank

5. News Industry - Trends HuffPost has consistently been an innovator in the digital publishing space. Biggest Social publisher

6. News Industry - Challenges

7. How Can Data Help ?

8. Ad campaigns International editionsSocial media promotion Editors User-experience Blog moderators Reporters HuffPost Studio

9. Content Lifecycle DistributionCreation Consumption

10. Content Creation: How Can Data Help ? ● Tools to help surface, discover trends in different parts of the web ● Content Enhancement with multimedia based on semantic matching (images, slideshows, videos) ● Optimizing headlines/images (RobinHood Platform)

11. Content Gap: Production Versus Consumption

12. Content Consumption: How Can Data Help? Know Your Audience ● User Cohorts: ○ Social Traffic versus FrontPage Clickers consume different content ○ Desktop Vs Mobile consumption ● Recommendations/Personalization ● Can we use data to inform product design and interface ? ○ Rearrange share buttons based on traffic origin (Facebook vs Pinterest)

13. Content Lifecycle DistributionCreation Consumption

14. Content Distribution: Can Data Help ? ● People’s attention is increasingly concentrated on social streams ○ More traffic to publishers from social than any other way ● Are Distributed Platforms the new home page ? ○ Facebook Instant, Apple News, Snapchat Discover, Google Amp ○ Messenger Bots ● You need to be where your audience is: ○ Identify the content mix that is maximally engaging on an external platform ○ Can we use data to seed these distribution networks ? (Facebook HuffPost Pages, Snapchat Discover)

15. Content Distribution: Can Data Help ? ● HuffPost produces 1000 articles a day - which of these do we promote ? ● Article PVs follow a very skewed distribution of success ○ Only 1% of our articles > 100k PVs ● Content performs differently on different networks. ● Can we predict the articles that will get traction in advance so ■ We can optimally seed multiple distribution channels (Facebook HP Pages, Snapchat Discover) ■ Target for premium/high value ads to maximize revenue ■ Populate Recommendation Widgets

16. Content Distribution: Can Data Help ? Challenges ● Histogram of traffic distribution - highly skewed. ● The very act of promoting something causes a bump in traffic. ● Data normalization - how long do want to wait before predicting ? ● Very imbalanced data set Our Approach ● Random Forest classifier. ● Multiple success criteria ● Historical examples of (+) and (-) articles. Downsampling. ● Different normalization thresholds ● Feature engineering: traffic growth ratios; initial organic social traffic per minute; distinct referrers;

17. Slackbot for the social promotion team ● 20% lift in PVs per predicted article

18. ● 20% lift in PVs per predicted article

19. Conclusion A Data Driven Newsroom today means ● More than just keeping track of clicks and shares ● Using predictive analytics to drive product and content placement Machine Learning will be a key driver for success with the advent of distributed content

20. Thanks ! MachineLearning@HuffPost

Geetu Ambwani, Principal Data Scientist, Huffington Post at MLconf NYC - 4/15/16

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Geetu Ambwani, Principal Data Scientist, Huffington Post at MLconf NYC - 4/15/16