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Building Real Time, Open-Source Tools for Wikipedia

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Building Real Time, Open-Source Tools for Wikipedia
with Rob Kenedi

Presented at FITC Toronto 2015
More info at www.fitc.ca

OVERVIEW
Wikipedia is one of the most frequently visited websites in the world. The vast online encyclopedia, editable by anyone, has become the go-to source for general information on any subject. Building user-friendly apps that people can actually use on top of Wikipedia’s massive dataset involves overcoming a number of challenges, but it can also be a lot of fun. Join Rob Kenedi, Entrepreneur In Residence at The Working Group (TWG) as he shares lessons learned in TWG’s Lab building WikiWash, a free tool for journalists that helps them uncover spin and bias in Wikipedia.

OBJECTIVE
Learn how to build useful products using Wikipedia’s dataset.

TARGET AUDIENCE
Software developers, data journalists, product managers

ASSUMED AUDIENCE KNOWLEDGE
What Wikipedia is, and how web applications work

FIVE THINGS AUDIENCE MEMBERS WILL LEARN
How we built an open-source tool for journalists using Wikipedia
How to manage the massive amounts of data in Wikipedia
How to turn a non-technical pitch presentation into a working product that the client loves
How TWG labs treats its projects, products and prototypes and what happens to them once they launch
How WikiWash can be used to expose bias and spin on Wikipedia

Published in: Technology
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Building Real Time, Open-Source Tools for Wikipedia

  1. 1. WikiWash: Ideation to Product A tool for uncovering spin on Wikipedia
  2. 2. Rob Kenedi Entrepreneur-In-Residence, @twg @rkenedi
  3. 3. 1. Context 2. The problem 3. TWG’s approach 4. The solution: WikiWash 5. Lessons learned 6. What’s next Table of contents
  4. 4. Context
  5. 5. Techraking Connecting journalists with technologists and designers to address problems around the world
  6. 6. The Problem
  7. 7. Today, some of the most relevant stories can only be told by poring over datasets and crunching numbers in Excel. It’s imperative reporters have tools to find the stories hidden in the data. -Luke Simcoe, Data Journalist, Metro News Canada “
  8. 8. Currently, English Wikipedia includes 4,852,854 articles. More than 800 new articles are added every single day. * *Source: Wikipedia
  9. 9. There is no political power without control of the archive, if not of memory. -Jacques Derrida (1998) “
  10. 10. TWG’s Approach
  11. 11. Unique Value Propositions Problem Spin is introduced into Wiki pages by biased edits Can’t connect edits with users, or uncover agendas / story angles Can’t get the data out of the system Hard to vizualize data to find patterns Can’t track changes to pages (relating to branded entities) Can’t find all brand references on Wikipedia Wikipedia perceived to be susceptible to biased revisions. Very hard to track revisions on Wikipedia, either historically or as they occur. Associate page edits with users, and download the data Ability to compare multiple pages to uncover patterns in edits, and download the data Ability to track activity and alert to edit activity / trends (that may indicate bias intent) No. pages ‘un-washed’ Number of connections / biased edits uncovered Number of edits to Wikipedia pages caused by uncovered biases Number of stories published siting data from the site Viral, word of mouth Partnerships with print / online media organizations - cross promotion Social media referrals PPC, Display, Email, SEO Clearly demonstrate connections between Wikipedia page edits and the users making those edits. Ability to track and uncover spin and malicious edits. Track page edits in near-real- time, and offer alerts that uncover trends and emerging stories. Developed by and for working reporters Reporters Activists Academics & Students Citizen Journalists HighUse$ PR & Media Brand Stakeholders Wikipedia Existing Wikipedia revision history page Wikistats Wikiwatchdog Article Revision Stats, Wiki Blame, etc IT Infrastructure Continuous reporting / scraping (unless partner up with Wikipedia) Marketing & Promotion Free for single use on historic data/edits Subscription model for activity alerts and real-time tracking (uncover breaking stories / bias) Competitors / Comparables Cost Structure Revenue Streams Solution Unfair Advantages ChannelsKey Metrics Customer Segments
  12. 12. Unique Value Propositions Problem Spin is introduced into Wiki pages by biased edits Can’t connect edits with users, or uncover agendas / story angles Can’t get the data out of the system Hard to vizualize data to find patterns Can’t track changes to pages (relating to branded entities) Can’t find all brand references on Wikipedia Wikipedia perceived to be susceptible to biased revisions. Very hard to track revisions on Wikipedia, either historically or as they occur. Associate page edits with users, and download the data Ability to compare multiple pages to uncover patterns in edits, and download the data Ability to track activity and alert to edit activity / trends (that may indicate bias intent) No. pages ‘un-washed’ Number of connections / biased edits uncovered Number of edits to Wikipedia pages caused by uncovered biases Number of stories published siting data from the site Viral, word of mouth Partnerships with print / online media organizations - cross promotion Social media referrals PPC, Display, Email, SEO Clearly demonstrate connections between Wikipedia page edits and the users making those edits. Ability to track and uncover spin and malicious edits. Track page edits in near-real- time, and offer alerts that uncover trends and emerging stories. Developed by and for working reporters Reporters Activists Academics & Students Citizen Journalists HighUse$ PR & Media Brand Stakeholders Wikipedia Existing Wikipedia revision history page Wikistats Wikiwatchdog Article Revision Stats, Wiki Blame, etc IT Infrastructure Continuous reporting / scraping (unless partner up with Wikipedia) Marketing & Promotion Free for single use on historic data/edits Subscription model for activity alerts and real-time tracking (uncover breaking stories / bias) Competitors / Comparables Cost Structure Revenue Streams Solution Unfair Advantages ChannelsKey Metrics Customer Segments
  13. 13. The Solution: WikiWash
  14. 14. WikiWash decreases spin and bias on Wikipedia, by holding those making changes accountable
  15. 15. How does it do that? • Realtime • Open source • Export your data • Free! • Works with Wikipedia’s API • Built in Javascript • Uses Node.js, Express.js, Angular.js, Socket.IO to facilitate involvement from others http://blog.twg.ca/2014/11/building-wikiwash/
  16. 16. wikiwash.org
  17. 17. Limited by API; caching data Lessons Learned Focus on realtime changes Trending articles aid understanding Focus on product first, aesthetics second
  18. 18. Wikitext
  19. 19. Wikipedia API Load Times
  20. 20. Preemptive Caching
  21. 21. Limited by API; caching data Lessons Learned Focus on realtime changes Trending articles aid understanding Focus on product first, aesthetics second
  22. 22. Limited by API; caching data Lessons Learned Focus on realtime changes Trending articles aid understanding Focus on product first, aesthetics second
  23. 23. Limited by API; caching data Lessons Learned Focus on realtime changes Trending articles aid understanding Focus on product first, aesthetics second
  24. 24. What’s Next?
  25. 25. • Notifications via email • Website embed capability • Access to Wikipedia’s firehose • UX improvements • Language support • Next / previous navigation Feature Roadmap WHAT’S NEXT
  26. 26. • Clear product ownership • Product / market fit • Pirate Metrics as a guide • Qualitative & quantitative feedback • Incrementally invest until inflection point How TWG Decides Next Steps WHAT’S NEXT
  27. 27. Fork the project on Github to improve it
  28. 28. github.com/twg/wikiwash
  29. 29. Send us your feedback, ideas and feature requests
  30. 30. wikiwash@twg.ca
  31. 31. Let’s talk about how to bring digital products to market or a live demo of WikiWash
  32. 32. Thank You @rkenedi rkenedi@twg.ca

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