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1345 track 1 chen_using our laptop

  1. 1. Measuring Success at the Edge of Innovation October 2017
  2. 2. 2 Good Afternoon! Sarah Schmalbach Guardian Mobile Innovation Lab - A small multidisciplinary innovation team of editors, reporters, a product manager and engineers in the Guardian US newsroom testing new mobile storytelling formats for two years with a grant from the John S. and James L. Knight Foundation with the goal of accelerating innovation in newsrooms around the country. Lynette Chen MaassMedia - MaassMedia is an independent, specialty analytics consultancy based in Philadelphia, PA. We provide guidance and leadership to major global brands seeking to optimize their investments in digital multi-channel content, marketing and customer service initiatives.
  3. 3. Slide 4-5 Slides 6-7 Slides 8-16 Slides 17-25 Slides 26-29 Slide 31 3 Agenda Overview Introduction The Experiments Measurement Framework NLP Putting it to Use Closing / Q&As
  4. 4. The Guardian Mobile Innovation Lab & Maass Media team Sarah Schmalbach Senior Product Manager Sasha Koren Editor Alastair Coote Developer Dylan Greif Product Designer Mazin Sidhamed Reporter Connor Jennings Developer Greg Kaminski Analytics Director Lynette Chen Senior Consultant Brian Hood Analyst
  5. 5. The team SME Data Designer Engineer Knows which questions to ask: Sarah Schmalbach Sasha Koren Mazin Sidhamed Dylan Greif Knows how to get the data: Alastair Coote Connor Jennings Brian Hood Knows how to present the data: Lynette Chen Greg Kaminski
  6. 6. 6 What do our experiments look like? Live video notifications Lock screen access to live streaming video news coverage without having to open an app, or wait
  7. 7. 7 What do our experiments look like? Live UK election results notifications Lock screen access to three types of live UK election results: overall, latest and by constituency -- plus tap-through access to more coverage and results
  8. 8. 8 The need for a new way to measure success Research Study conducted by: & Overheard in the industry... The bigger thing is, how do we measure successful alerts? Because we’re not sitting next to everyone as their phone goes off saying, ‘Now did you open that? Did you get everything you need to get without opening that? Did you appreciate getting it?’ It’s just sort of an impossible thing to measure without doing a really wide ranging survey, which we’re not going to do.” -Mobile Editor, News Agency
  9. 9. Determine which formats for delivering content are the most successful 9 Success framework Quantitative Data Qualitative DataEngagement Behavior User Opinion
  10. 10. 10 Quantitative : What did users do? Total Interactions
  11. 11. 11 Quantitative : What did users do? Positive vs. Negative Engagements
  12. 12. 12 Quantitative : What did users do? Net Interaction Rate
  13. 13. Net Interaction Rate 13 Quantitative : What did users do? Number of positive interactions – negative interactions Number of notifications shown
  14. 14. 14 Qualitative: What did users think? User surveys • 24 user surveys sent • Range of 5 to 6,219 responses
  15. 15. 15 Need to automate qualitative analysis Longform response feedback May 2016 x5 responses Analysis in 75 seconds Nov. 2016 x1,116 responses Analysis in 24,165 seconds Q: “Anything else you’d like to tell us?”
  16. 16. 16 Opportunity with sentiment NLP • Smart manual analysis of longform feedback was no longer possible • Needed a way to speed up the process but also keeping the scoring consistent • Wanted to understand overall sentiment by evaluating the sentiment of each response through identifiable keywords • Solution: Natural Language Processing (NLP) for sentiment analysis!
  17. 17. 17 Process of Building Create the algorithm Obtain a dataset Manually score sentiment of data Feed some of the data for the algorithm to be trained on Use the trained algorithm to predict results of remaining data Review results, identify areas of concern, iterate
  18. 18. 18 Our original algorithm • Inspired by various existing NLP python packages • Negation words • Exclude “noise” words • Trained on 500 US Election experiment survey long form responses • Accuracy on US Election data: 81% • Issue: limited scope
  19. 19. 19 VADER sentiment algorithm • Developed by Georgia Tech • Trained on a 7,000+ word lexicon • Takes into consideration punctuation, emojis, and amplifier words
  20. 20. 20 VADER sentiment algorithm - valence • Each word is assigned valence on a scale of -4 to 4 Word Valence abusive -3.2 idk -0.4 zebra 0.0 good 1.9 ☺ 2.0 great 3.1
  21. 21. 21 Adapting the VADER sentiment algorithm • Lexicon covers a wide range but isn’t complete • Updated, updating, up-to-date • Vanish, vanished, vanishing • Updated with Guardian specific words and permutations • convenient • “become a contributor” • “will purchase” • Accuracy on US Election data: 80% • Accuracy on EU Referendum data: 88%
  22. 22. 22 VADER – positive sentiment example “Jason is smart, handsome, and funny.” Sentiment Score negative 0.00 neutral 0.25 positive 0.75 overall positive
  23. 23. 23 VADER – negative sentiment example “Jason is not smart, handsome, nor funny.” Sentiment Score negative 0.65 neutral 0.35 positive 0.00 overall negative
  24. 24. 24 VADER – nuanced sentiment example “Jason is not smart, handsome, but he is funny.” Sentiment Score negative 0.26 neutral 0.45 positive 0.29 overall positive
  25. 25. What do users think? 25 A new KPI : net sentiment score Number of positive responses – negative responses Number of responses
  26. 26. 26 UK Snap Election: How did users engage? • Positive interactions: taps and switch views • Negative interactions: unsubscribe and stop alerts • Net interaction rate: 3.4%
  27. 27. 27 UK Snap Election: What did users think? Net sentiment score: 44% 105 269 Negative Positive Survey Responses
  28. 28. 28 UK Snap Elections: identifying key responses “I loved the live general elections updates, found it easy to use and loved the expandable visualisation. It was great … I used the alert to check on the progress of the election throughout the night. Would definitely use it again” “I live in Australia and work as a chef. I cannot take breaks and don't have time to Google results. It was excellent to have the live updates on my alert screen…all I had to do was tap the screen to see updated results. It was fantastic. Thanks Guardian. Great work.” “The alerts kept causing my Android Chrome browser to crash.” 97% 97% -40%
  29. 29. 29 UK Snap Elections: evaluating success • Overall success with the alerts • High net sentiment score • Positive net interaction rate • Signals that alerts can provide utility on their own and also give convenient access to deeper coverage 44% Net Sentiment Score 3% Net Interaction Rate
  30. 30. 30 Learnings/Takeaways Have a toolkit with both quantitative and qualitative data 1 2 3 4 Be forward thinking and flexible Building/adapting an NLP algorithm is an investment but can provide great utility Need to adapt and improve NLP algorithms over time
  31. 31. 31 Questions? Sarah Schmalbach | Guardian Mobile Innovation Lab Senior Product Manager sarah.schmalbach@theguardian.com @schmalie Lynette Chen | MaassMedia Senior Consultant lchen@maassmedia.com @huirastic

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