Political TV ads: not what you think they’re about

Team members: Kalki Seksaria, Gary Burnett, Michael Drachkovitch, Argyro Nicolaou

The data say that the top topic choices for political TV ads in Iowa did not always match the issues voters considered to be the most important in that state. We want to tell this story because it provides insight into the logic of political TV ads during a primary, where the emphasis is less on pitting the voters against the rival political party but more about differentiating same-party candidates and getting voters to the polls.

We mainly worked with the Political TV ad data set but also used data from the CNN entrance polls from the Iowa primaries.

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We first had to clean up the data set, giving each topic its own column, since the ‘topic’ cells were populated with every topic mentioned in each ad. After aggregating the number of times each topic came up, we picked the top five issues for each party. In order to attribute ad affiliation as either Republican or Democrat we worked with the Sponsor column and not the candidate column since the latter included every candidate mentioned in the ad. We made a list of each of the Sponsors and researched their affiliation. Our charts exclude unaffiliated, non-profit donors (there were only two such organizations that advertised in Iowa anyway).

Having done this work, we used Tableau to create a line graph per issue per party (10 graphs total) mapping topic against time.

The CNN entrance polls on the Iowa primaries were used to make a bar chart of the timing of Republican and Democrat voters. Since the timing offered by the exit poll survey was under categorical values: ‘Today’; ‘last week’; ‘last month’; we had to decide which range of dates to include under each of these terms, to make sure that a relationship existed between the two datasets. Kalki describes the process: I first converted the categories into dates. Today = 2/1/16 (Iowa Caucuses Date). Last few days = the 2 days before the caucuses. For before last month, I assumed it meant between 1 and 3 months ago. I then assumed that the number of people who decided in a time window were evenly distributed over that time window. For example, if 30% of people decided last month, and “last month” included 23 days (30 day month – last 7 days are  “last week” or shorter), then 30% / 23 = 1.3% decided each day.

Our choice to present the most popular ad topics and most important topics according to voters as a table aims at pointing to the unexpected discrepancies between the two sets of information. What other reasons could there be for pushing a specific ad topic, even if voters don’t think it is important? To try to understand this, we plotted each party’s top-5 ad topics as a line graph against time and superimposed an area chart that maps the timing of voter decisions in order to see whether there is a correlation between certain ad topics being blasted out to voters and when the voters made up their mind.



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

World AIDS Day, 25 Years Later: What Have We Learned?


This is an infographic produced by the ONE Campaign, an international campaigns and advocacy organization dedicate to ending extreme poverty and preventable disease in Africa, that tells the story of the progress made against HIV/AIDS over the past 25 years (this post was published in 2013) and what’s needed to rid the disease from the planet.


The data shown next to the image of Africa illustrates the progress select African countries have made against AIDS by measuring the ratio of people newly infected over the people newly added to treatment (assumed an annual measure) and introduces the “tipping point” as the moment when the total number of people infected is equal or less to the number of people newly added to treatment. They then categorize the select African countries into four buckets: (1) Reached the Tipping Point; (2) Close to the Tipping Point; (3) Acceleration Needed; and (4) Progress Reversed. From the data presented, it’s clear that while many countries have made great progress or are on there way to reaching the tipping point (21 countries), there are still 16 countries where acceleration is needed or progress has been reversed. To illustrate this difference, the infographic compares the state of HIV/AIDS in two countries: Cameroon and Ghana.

The audience for this report U.S. policy makers and international development officials.

The goal of infographic is to drive home the message that while progress has been made, significant challenges lie ahead. And the use of data around HIV/AIDS deaths is a powerful reminder of the difference that smart, effective and accessible interventions can have.