#NeverTrump: The Candidates are On Board, but What About the SuperPACs?

By Judy Chang, Iris Fung, and Eric Lau

The data say that SuperPACs supporting non-Trump candidates are more interested in attacking other non-Trump candidates than Donald Trump. We want to tell this story because it stands in stark contrast to the #NeverTrump movement.

The news is dominated by the 2016 election, specifically by the negativity of the candidates’ campaigns. One of the chief contributors to this negativity are SuperPACs, which wield unlimited political spending power in support of their favored candidate. We focused on exploring the data behind the SuperPAC efforts directed against Republican front-runner Donald Trump. So far, Trump has been the focus of concerted verbal attacks by Marco Rubio and Ted Cruz in the televised debates. However, after exploring the Political TV Ad Archive’s dataset in Tableau, we found that this did not hold true in SuperPAC advertising. For example, the majority of the ‘con’ ads were purchased against Rubio and sponsored by Right to Rise, a SuperPAC supporting Jeb Bush. This was surprising. For all the talk of the Republican establishment’s dislike of Trump, the data suggested that there was even more internal discord among themselves.

We wanted to express this disconnect between the aforementioned intent and execution in an intuitive way. To do this, we built a hybrid infographic/interactive chart/article. Text snippets guide the viewer. A smaller bar graph shows current delegate numbers from the Associated Press. This recognizable, friendly graphic eases the viewer into a more technical chord diagram, which is not a commonly seen chart format and requires the reader to spend more time on it to understand the relevant relationships. We chose the chord diagram over a normal bar graph, because the directional arrows and arrangement of candidates around the circle connoted the feisty conflict of the campaign. Our infographic article combines humor and data to give the reader a non-obvious insight on the Republican race.

Check it out here!

Activity Log: Sat 2-6-2016

Reem’s activity log in chronological order:

1- Sleep tracked by sleep app.
2- text and calls tracked by phone.
3- shower timed manually.
4-steps tracked by phone.
5- lunch at Korean place (tracked by yelp app and credit card)
6- what I spend on credit and debit card.
7- google search (tracked by google).
8- Dinner with friends (video snaps on snapchat).
9- Movie online (tracked by amazon).
10- Sleep tracked by sleep app.

Activity Log: Feb 8, 2016

I chose yesterday, Monday February 8th, for the log because it’s my day with the most activities over the weekend. In chronological order,

  • Worked out
    • Number of steps, distance, pace – iPhone
    • List of exercises – my own log
  • Organized Gmail and Calendar
    • When I’m available this coming Sat/Sun – answered a WhenIsGood
    • My weekly schedule – added time commitments on Google Calendar
  • Youtube and Netflix surfing
    • What I watched – Youtube/Netflix uses my history to make recommendations
  • Lyft’ed to the Hawthorne
    • Where I was and where I’m going
  • Snapchatted a bunch
    • How many pictures I took and who I’ve sent them to
  • Bought some drinks
    • What,when, and how much I bought
  • Uber’ed back to Apartment
    • Where I was and where I’m going
  • Venmo’d friend for Uber
    • Who and how much I charged
  • Uber’ed to House of Blues
    • Where I was and where I’m going
  • House of Blues Staff scanned my concert ticket
    • That I attended the concert and when I entered the venue
  • Bought some more drinks 
    • What, when, and how much I bought
  • Snapchatted even more
    • How many pictures I took and who I’ve sent them to
  • Uber’ed back to Apartment
    • Where I was and where I’m going
  • Venmo’d friend for concert and Uber
    • Who and how much I charged
  • Called parents on Skype
    • Who, how long, and when I made the call
  • Throughout the day: Facebook surfing
    • Who/when/where/what of posts I liked, videos I watched, posts I’ve seen, and comments I’ve made
  • Throughout the day: Texting
    • Who/when/what I texted
  • Throughout the day: Listening to Spotify
    • Who/when/what music I like, dislike, follow, and etc.
  • Throughout the day: iPhone Walker
    • Where/how much/when/how fast I walked from place to place

Activity Log for Monday, February 8th, 2016

  • Entering buildings tracks usage of my MIT ID to tap or swipe in
  • Ate lunch at Maseeh, swiped my ID to keep track of my meal plan
  • GPS tracking was enabled on my phone in the morning (I turned it off after remembering about this assignment)
  • Called my family after dinner
  • Various websites tracked me online as I surfed (I use a bevy of add-ons to block as many as I can)
  • Wrote a bunch of code: this involves making commits and pushing (uploading) them to an online repository (GitHub.com and GitLab.com)
  • Sent text messages to friends and family via Signal (which performs opportunistic encryption)
  • Watched a TV show in my floor lounge
  • Sent a variety of emails, archived and deleted other emails

Data Tracking Log – Weekend Activities

Saturday, Feb 6

  • Electronic alarm clock (iPhone)
  • Read news on iPhone (NYT, Facebook, emails)
  • Put on smart watch (Apple watch)
  • Walked to gym and back (phone GPS)
  • Listened to Spotify en route
  • Used MIT Card to gain entry to the gym
  • Used MIT Card to gain entry into the locker room
  • Used smart watch to track workout
  • Spoke with family and friends over the phone (length of calls, contacts, what was said?)
  • Skyped with a friend
  • Organized dinner with friends over text message
  • Google Chrome tracked email and web traffic
  • Paid for dinner via credit card
  • Ubered to bar after dinner
  • Paid for round of drinks with credit card
  • Ubered home
  • Set iPhone alarm clock for next morning
  • Charged phones, smart watch and laptop

Kendra’s Activity Log 2/8/2016

eyes

  • Road the bus and the T: data tracked via swipes of my Charlie card
  • Google searched: number of article research, my minds own curiosity.
  • Podcasts: downloaded podcasts, iTunes for sure tracks how many podcasts I download and whether I’ve listened to them recently (If I haven’t it’ll stop downloading new ones of the same podcast).
  • General web surfing: link clicks.
  • Facebook: length of data sent through messaging, messages written but not sent, whose profiles I looked at, what content I liked, and what if any links I clicked on.
  • Sent emails: using Gmail and MIT’s web based outlook, recorded data on who I sent the message to (to help improve spam filters), likely the content of the message (checking for “trigger” words that indicate problematic content, either spam worthy or you know terrorist watch list worthy).
  • Twitter: made tweets, re-tweeted, and liked content.
  • What’s app message: messaged friend in Canada.
  • Gchat: number of people I chatted with, length of messages
  • Google map: location data ( I get lost a lot)
  • Netflix: streaming video data on what shows I watch
  • Plex: streaming video data on how many “home videos” I watch (I don’t sync my video data to metadata so it’s unclear how much Plex actually knows about the content of what I watch vs the quantity)Spam data (how successful was my spam filter)
  • Diet bet app: my weight.
  • Grocery shopping: consumer spending data recorded twice in this case, by the supermarket where I made the purchase and by my credit company which also tracks that data.
  • Phone call:  location data (based on the cell phone tower that my conversation pinged off of), duration of call, and whether it was domestic or long distance (it was domestic).
  • Youtube: which videos I watched, and which ones I watched to completion, also their ads – did I stick it out to the end or did I click on the countdown link. Uses the data to suggest other videos I’ll watch.
  • Pandora: which songs I listened to the end, which ones I liked, which songs I skipped, and which ones I asked them to never play for me again.

Basically, every waking moment of my lie is quantified except for what I eat, and my apartment’s thermostat. However, the electric company is tracking the kW of electricity my home is using, while the gas company is tracking how much gas I use.

Data Log for Monday February 8th – Argyro Nicolaou

In chronological order:

1. Sent Whatsapp messages to 2 friends
2. Screenshot from pdf in dropbox saved to evernote x2
3. Created new Note in Evernote
4. Google search ‘Fetty Wap House of Blues’
5. Google search ‘Post Malone’
6. iMessage x 30
7. Submitted a cross registration petition to Harvard’s Registrar
8. Called Registrar
9. Made calendar entry for meeting on Feb 23
10. Sent email to SZ
11. Create vocabulary quiz for students on MWord
12. Sent ERW (tutee) an email re: our class tomorrow
13. Created Unit 16 vocabulary document for students
14. Upload Unit 16 vocabulary on canvas
15. Emailed advisor
16. Took an Uber survey
17. Google search for a greek word
18. Phone call w VP in NYC
19.  Liked posts on FB
20. Google searched ‘Charlemagne’
21. Unsubscribed from Nordstrom email list
22. Clicked through FB post to imgur and a greek website
23. Opened NYTimes mobile app
24. Opened twitter app; retweeted NYTimes World tweet
25. Sent documents to Harvard library
26. Used FB messenger app
27. Clicked on web link in email
28. Used Uber app to get cab x 2
29. Google searched ‘Classical Greece and the Mediterranean’
23. Tweeted x3
24. Clicked through MSNBC tweet & watched MNSBC video

Activity Log of Data Created on Saturday, 2/6/16 – Eric Lau

I took Saturday off to cook food but still generated a wide variety of data.

  • Query, temporal, and text data of my online actions on the Google platform: checked, cleaned out, and sent email via Gmail; watched some videos on YouTube; used Google Search; and recorded this list on a Google doc.
  • Address data for grocery delivery by Peapod: attempted to buy groceries online through the Peapod website (before realizing I needed them today, not tomorrow).
  • Fare transaction data for the MBTA: took the 1 Bus to and from Shaw’s/Star Market.
  • Coupon selection history data for coupons.com: Looked at coupons for good deals while on the bus.
  • (Inadvertent) picture data for Snapchat: Was very likely captured in photos when the person in front of me on the bus decided to take Snapchat selfies that included everyone behind her.
  • Music history data for Apple Music: Listened to a mix of old and new songs on the bus.
  • Credit card financial data: Paid for groceries at Shaw’s/Star Market.
  • Time and location access data for my MIT ID: Tapped ID to get back into Burton Conner.
  • SMS/iMessage data: Sent texts to friends and family throughout the day.
  • Call detail record data: Generated for Verizon when I called my sister in the afternoon.

Kenny’s Data Log: 1/7/16

On my way from Providence, RI (spending the day at HackAtBrown), I logged my data (or, at least as much of it as I could).

Travel

  • Location on Public Transit: Tapped MIT ID to use T
  • Purchase Information: Used a credit card to buy train tickets.
  • Geo-location apps also know my GPS location

Hackathon

  • Check In: Signed into the hackathon in the early afternoon
  • Internet Traffic: Using my computer at the computer throughout the day tracked by Brown and their ISP
  • Progress by teammates: My teammates tracked the progress of my portion of the project
  • Music over time: I listened to Apple Music throughout the day while working

Throughout the Day

  • Heart rate over time: Through the Apple Watch, that I wear on a daily basis
  • Step Count over time: Also through the Apple Watch
  • Motion Tracking: The Apple Watch also does motion tracking, and records how often I stand up throughout the day
  • Emails & Text Messages over time: My communication logs with people not near me through email and texting on my phone and computer.
  • Various Internet services: Youtube, Netflix, and Twitter track my usage, which occur throughout the day

Catherine’s Log 2/7/16

  • Watched SuperBowl: TV Viewership, RCN
  • Researched concussion science on the internet: google search history
  • Took the subway: MBTA
  • Swiped into MIT buildings after hours: MIT Log
  • Walked around Harvard using phone GPS for directions
  • Recorded an interview with TapeACall: Saved on server, also AT&T
  • Used electricity for lights, charging electronics: N-Star
  • Phone checks email and social media: facebook, twitter, Instagram, tracks location
  • Sent emails: Google, Outlook
  • Phone tracks steps, minutes active, location
  • Water usage for showering, hand washing, etc: City of Somerville
  • Booked a tripàflight, car rental, hotel: Delta, rental company, hotel, credit card
  • Listened to music on Spotify
  • Watched Netflix
  • Watched Hulu
  • Made phone calls and sent texts: AT&T