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MIRRORED
/asknate
/asknytelection
Alastair Coote
Alastair Coote
Alastair Coote
Alastair Coote
Alastair Coote

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Alastair Coote

Editor's Notes

  1. a small experiment with the R&D team, an installable bot that bring election coverage into your Slack room intended to publish to a small number of friends and techies well meaning colleague tweeted it out, and…
  2. Nieman Lab found it before we knew it, in some 500 Slack rooms large audience made it more daunting started doing live coverage on primary nights how did we do that workflow? we used Slack itself as an admin
  3. inspired by other work we’d done for live chats on live blogs Separate room dedicated to bot posts Publishing was controlled via emoji responses Or at least, it was until the incident…
  4. - put an extra layer of security over it
  5. - put an extra layer of security over it
  6. CMS for the Slackbot was actually Slack itself Separate room dedicated to bot posts Publishing was controlled via emoji responses Can see these highlighted posts…
  7. …are then seen in the output room Names of authors stripped out, posting “as” the bot But this wasn’t always the case
  8. there was a separate emoji reaction using that posted as the user… Paul Volpe - Deputy Washington Bureau Chief
  9. … like so primary night coverage was OK, but most Slack audience at home also very busy, difficult to carve our resources
  10. instead, let’s make an event - office hours Nate Cohn - Reporter for the Upshot
  11. a live chat broadcast on our site and mirrored by the Slack bot one problem, though…
  12. there was a lot of it live for an hour. some users loved it, some hated it realized we don’t know the context of our bot, what room is it in? how many people? one success, though…
  13. one success, though: questions added a slash command, /asknate, piped questions into our Slack room which we could then select to publish back out again as Nate answered. Got us thinking about questions - existing command just forwarded to the newsroom
  14. worked again with R&D to try natural language processing of questions they created a dataset with some candidate positions and election calendar
  15. used wit.ai, an NLP service bought by Facebook has a mix of entities, intents and expressions
  16. put them all together and you can see how it pulls out various terms from questions this structured data is then sent back to us to process
  17. worked great for the first five minutes or so very quickly realized that people’s questions would be numerous and diverse
  18. some of them are answerable - for example, get donation data, good to go most are not, kind of questions people asked Nate. Requiring deep analysis - which is why we’re here! this does. not. scale. supreme court example: is that an election question? what comes after that? is “I don’t answer that” a valid response? you would need a full time team answering questions, and even then, would maybe fail nature of news means dataset is always changing. Apple can afford to tweak Siri endlessly, we’d be starting over and over
  19. within the confines of the experiment we weren’t able to do that justice so we shut it down for now. Excited for the future, but a chat experience like this has to be very, very focused. Maybe Olympics - data-driven event, lots of people in the office during the day