Databases 

Reporting & Writing ✏️

Public Opinion and Polling

Part 1: What Happened To The Polls In 2016 — And What You Should Know About Them In 2020

Part 2: What Makes A Good Pollster?

Media

Other stuff...

Newsroom Projects 📰

Editing 📝

Editing Data

https://data.fivethirtyeight.com

#opensource

#opendata

Quantitative Editing

Editing Words

I edit Fivey Fox ... which is usually just FiveThirtyEight's politics intern

https://twitter.com/fiveyfox

I am also an editor for "Points Unknown"

(GSAPP @ Columbia) 

Bots 🤖

2016

Bot reports the facts, leaving time for humans to interpret them.

2018

But the bot also helps interpret facts!

2018

Lets readers see results that FiveThirtyEight deems unexpected

Expectations are calibrated before results ever start coming in.

2020

Bot evolves into a human...

...jk

More Complex Bots

http://journalism.stanford.edu/cj2016/files/Newsbots.pdf

http://journalism.stanford.edu/cj2016/files/Newsbots.pdf

Generalized Bot Architecture

C+J Conference @ Stanford (2016)

Scraping 🕷

Visualization 📊

Research

🔬

Mayor Pete is Smart and Elizabeth Warren is Unlikeable: A Text-As-Data Approach to Studying Media Representations of the 2020 Democratic Presidential Primary Candidates

panel: Gender and Political Communication

Midwest Political Science Association (2021)

Domain Specific Newsbots: Live Automated Reporting Systems involving Natural Language Communication  

Computation + Journalism (2016)

A Computational Approach to Studying Framing in Political Rhetoric
panel: Applications and Advances in Text Analysis and Machine Learning American Political Science Association (2014)

Computationally analyzing text to better understand media and political environments.

I have a research interest in text analysis

Why?

Because it is another tool for

empirically guided inquiry,

which is common to Journalism and Social Science.

Narrowly

I have an interest in text analysis.

Broadly

I am interested in finding journalistic applications for methodologies in quantitative social science.

Data journalism:

"Quantitative social science...on deadline."

- Andrew Flowers (former 538 quantitative editor)

But the "on deadline" part can be tricky...

Which is partly why methodological innovation is so hard to do in newsrooms, and often the new and innovative methodologies are limited to large newsrooms with time and space to experiment or with the power to put resources behind large enterprise projects.

... and even then we're often leaning on the work of our colleagues.

• What has it been used for in Social Science?

• What is it not good for?

• What are the pitfalls?

Using a new methodology:

• What has/could it be used for in journalism?

• What are the pitfalls specifically to journalistic inquiry?

• How do we edit a story that uses this methodology?

• How do we communicate the methodology readers?

• How do we communicate the pitfalls to readers?

It's hard for newsrooms to do this kind of work regularly without a template for it.

• There is only so much methodological innovation our editorial process can handle. The edit-burden for introducing a new methodology has to be justified by the importance or the story due to deadline / bandwidth constraints.
   
• There is an "uncanny valley" of stories that could push the envelope in terms of methodology that can't get done because they require more innovation than can be justified (the first time).

Journalism Schools are well-placed to pioneer methodological innovation.

• Co-location with academics of from disciplines makes taking inspiration from other disciplines easier
   
• It's exciting work for student projects (and makes for great portfolio pieces)!
   
• Students often spend several weeks on a project...there is room for iteration.
   
• J-schools are well placed to take knowledge from across newsrooms who are doing this kind of work, and consolidate / explain / democratize / "templatize" it for newsrooms.

Dhrumil's Classroom

Sticky Notes Everywhere!

Lesson Planning

• Lessons are strictly tethered to learning objectives.
   
• Class is built to promote conceptual understanding ("learning how to learn")
   
• Assignments force students to apply those skills and learn things on their own

Student Feedback

• Daily Standup Meeting (class & projects)
  • What have you done since the last class?
  • What do you plan to do between now and the next class?
  • Any blockers?
     
• Sticky Notes for Workshops
  • Blue when you're done, or feeling a sense of mastery
  • Red when you're stuck, or feeling lost
     
• Parking Lot Questions

Learning Together

• Coding "I do" vs "You Do"
• Pair programming
  • Driver - Writing the code
  • Navigator - Figuring out what to do next
• Project-based learning
• An active Slack group...

Inclusion

• Fear of technology
• Systems like Blue / Red sticky notes in place to promote inclusion...no student should feel this is not for them.

"Dhrumil did a fantastic job of not only lining up great speakers for the class, but achieving gender parity among his guests - an impressive feat in the male-dominated tech sphere!" 
---
This class gave me more confidence to pursue possible jobs in being a bridge between technology teams and social service delivery. It also sparked my interest in mastering some skills in the course we didn't get to practice but seem valuable, like scraping for data or utilizing and API.

---
I gained confidence that I am able to get to grips with new technologies/ software on my own and tools for doing so in a systematic way.

Rigor

This was a great class and I really enjoyed taking it! I had to work surprisingly hard, but felt like that learning was purposeful. I feel proud of my final project and am grateful to have had the opportunity to work on it.

---

Appendix

Example 1:

"Research shows that minority candidates can be successful in drawing out co-ethnic minority voters....

...but it is difficult to draw any conclusions from the research about national elections, in which partisanship is a much stronger force."

During Jindal’s first gubernatorial campaign, in 2003, South Asian-Americans donated an estimated $667,000, or 19 percent of the $3.5 million he raised from individual donations.

During his much more expensive 2007 and 2011 campaigns, that figure dropped by about half and made up only about 4 percent of the approximately $8 million that he raked in from individual donors during both of those campaigns.

Harvard Kennedy School

Democracy and Technology Fellow (Ash Center for Democratic Governance and Innvation)

DPI-691M | Programming and data for Policymakers

Understanding enough about technology to not be fooled by people selling bad technology or charging too much for too little...

... or perpetuating bad ideas about technology like "security through obscurity"

Not deferring technical decisions to "technical people"

Unlocking Quant Skills

Data in the Classroom

Data in Journalism

Text

New research from the University of Washington finds that a natural aptitude for learning languages is a stronger predictor of learning to program than basic math knowledge, or numeracy.

- University of Washington News

March 2, 2020

DPI-691M: Programming and Data for Policymakers

(aka. #code4policy)

Data and code are no longer just for programmers. Policymakers in the 21st century, from members of congress to analysts and executives need to be equipped with the necessary skills to navigate nuanced issues at the intersection of technology and governance.

Those who have first hand experience with programming, data, software development and management, methods, open source collaboration, and technology innovation are better prepared to competently navigate these issues.