Scaling Credible Content

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This document discusses scaling credible content production by finding authors at scale. It describes a two-round operation to identify authors: first attempting to find authors from big publishers using data crawls and lists, but needing a bigger scale approach. The second round tapped into big data sources like Common Crawl and GNIP to build an application indexing domains for authorship markup, using Amazon Cloud Search and Giraph to rank authors at large scale. The result was tools to find and rank authors called VoiceGraph and VoiceRank.

Technologie Bildung

Scaling CREDIBLE Content
A Case Study
by Joe Griffin
Co-CEO, iAcquire

Problem: Finding CREDIBLE Content
Producers at Scale is Tough

Within search results, information tied to verified online profiles will be ranked
higher than content without such verification, which will result in most users
naturally clicking on the top (verified) results. The true cost of remaining
anonymous, then, might be irrelevance.
– The New Digital Age by Eric Schmidt, Chairman of Google

Operation Find and Rank Authors
- Round 1 – Low Scale

We Stalked Big Publishers That
Use Google Authorship

Nerdy Data Crawls Millions of
Websites & You Can Search HTML

Example – 55,000 Websites Found Using
the Google Maps API

Blekko Published a WebGrep on
Rel=Author – Top 500 Free

We Created Big Lists of Credible
Authors (like 100,000)

But, We Needed to Judge Their Relative
Proliferation…
Because Google Authorship is Becoming “People PageRank”

Operation Find and Rank Authors
- Round 2 – Big Scale

We Tapped into Big Data
& Built an Application
Big Data + Software Engineering = Scale

The Common Crawl Became a Seed Source
41 Million Domains, 4 Billion Pages and 210 Terabytes

GNIP Served as a Real-Time Engine
Firehoses from Twitter, Tumblr, WordPress, and more…

Example – You Can Pull Up to 2-Years
of Historical Tweets
Firehoses from Twitter, Tumblr, WordPress, and more…

We Used Amazon Cloud Search to Create
an Index of the Domains
Specifically URL’s that have authorship markup.

We Used GiraPh To Rank The Authors
Facebook Uses Giraph Too

Follow iAcquire™
and ClearVoice™ for
Tools and Resources

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Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?

Driving Behavioral Change for Information Management through Data-Driven Gree...

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Scaling Credible Content

1. Scaling CREDIBLE Content A Case Study by Joe Griffin Co-CEO, iAcquire

2. We do Scalable Content Marketing

3. Problem: Finding CREDIBLE Content Producers at Scale is Tough

4. Within search results, information tied to verified online profiles will be ranked higher than content without such verification, which will result in most users naturally clicking on the top (verified) results. The true cost of remaining anonymous, then, might be irrelevance. – The New Digital Age by Eric Schmidt, Chairman of Google

5. Okay, How Do We Find These Authors?

6. Operation Find and Rank Authors - Round 1 – Low Scale

7. We Stalked Big Publishers That Use Google Authorship

8. Nerdy Data Crawls Millions of Websites & You Can Search HTML

9. Example – 55,000 Websites Found Using the Google Maps API

10. Blekko Published a WebGrep on Rel=Author – Top 500 Free

11. We Created Big Lists of Credible Authors (like 100,000)

12. But, We Needed to Judge Their Relative Proliferation… Because Google Authorship is Becoming “People PageRank”

13. Necessity is the Mother of Invention

14. Operation Find and Rank Authors - Round 2 – Big Scale

15. We Tapped into Big Data & Built an Application Big Data + Software Engineering = Scale

16. The Common Crawl Became a Seed Source 41 Million Domains, 4 Billion Pages and 210 Terabytes

17. GNIP Served as a Real-Time Engine Firehoses from Twitter, Tumblr, WordPress, and more…

18. Example – You Can Pull Up to 2-Years of Historical Tweets Firehoses from Twitter, Tumblr, WordPress, and more…

19. We Used Amazon Cloud Search to Create an Index of the Domains Specifically URL’s that have authorship markup.

20. We Used GiraPh To Rank The Authors Facebook Uses Giraph Too

21. The Result

22. VoiceGraph™ and VoiceRank™ ™

23. Follow iAcquire™ and ClearVoice™ for Tools and Resources

24. You Stay Classy Manhattan

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