Methods for Dating, (App)lied

Check out the story here.

This project evolved from a seedling thought of a game show-like survey, with Chelsea meeting up with her Tinder matches, to a data driven piece. Think Bachelorette sponsored by Tinder, with the addition of the dates not knowing they were attending a public event, not a private date. We reinvented the data we gathered from the Tinder matches, and instead looked for trends in the conversations. For example, our world cloud on pick up lines. We wanted to see who was swiping, how old they were, and what they wanted to get out of the match (a date or a hookup). We swiped on everyone we saw for about a month, and scraped our data on those who matched with us.

Our focus morphed into: Why do people use these apps? From the the data we scraped we distilled it to two options. To date, or just for casual relations. We acknowledge but do not account for the margin of error with those who fall in and out of love with their matches, to superficial turned soulmate or vice versa.

Here is what we did with the data:

  1. Started with an exported version of our google forms survey with 71 respondents. Obviously, kind of messy just because we gave subjects a lot of room for testimonials.

  1. Due to the testimonials and freedom of writing responses, the excel spreadsheet should be downloaded into OpenRefine for further cleaning.
  2. Dating, neighborhood, student or full-time, and especially dating application preference needed to be cleaned as people either responded or had vague responses.

  1. From there the data was imported into Tableau.


  1. First, we wanted to see what the preference for dating apps were for people. So, we created some bubble charts to present size.
  • Tinder is the most popular dating application used at 31 people
  • Bumble is second place at 8 people
  • OKCupid is 7 people
    • The data set does have an error though because Okc and OkCupid are most likely the same thing
  • The data set may need to be cleaned though people were allowed to list their preferences so that jarbled the data set as well.
  • Many people also had no preference or used multiple apps
  • This format it is in is to help aide audience members in visualizing the size of each dating app preference. This also makes it more exciting to look at than a bar graph
  1. It’s important to note who wants to date and who wants to just hook up and where age plays a role. Age is an important aspect here as we have delineated age groups that are either active with the apps or not or it could be vice versus.

  1. We also explored age and the success of actually dating since we noted that the majority of people are actually looking to date using these apps more than actually hooking up.

  1. Though people are using these apps for dating there are some who are using the apps for hooking up strictly, so we decided to check out how often people were hooking up with these apps.  

  1. It was interesting to combine the two ideas of dating versus hooking up and what location had to do with it, which it didn’t show initially, but this could be an error that can be fixed or could be explored more thoroughly.

  1. In regards to Chelsea’s personal profile we thought it would be interesting to explore the introduction lines and see if there was a trend in opening lines whether it was age, location, job, etcetera.

Word Cloud:

  1. Copied the “Opening line ‘keyword’” column from the Tinderette Matches spreadsheet and pasted them into an empty word document. This word document was then saved.
  2. The saved word document with the opening line data was uploaded to Overview. By default, the tool generates a word cloud based on the frequency of a word in the documents uploaded of a given project.
  3. The web browser running over was zoomed in to so the word cloud was as large as it could be displayed on the computer monitor.
  4. The Snipping Tool app (a standard program on all Windows OS computers) was used to capture a high-resolution PNG file of the word cloud to be used in the final narrative.

App Icon Bubble Chart:

  1. Exported a PNG file of the Bubble Chart from Tableau into Photoshop to use as an overlay to preserve the ratio between the different apps.
  2. Downloaded high-solution/vector images of the app icons from Google Images and imported them into Photoshop.
  3. Using the Ellipsis Marquee Tool to select the app icon, each icon was added to the it’s corresponding spot above the background layer. The Scale Transform tool was used to make each icon fit within its corresponding circle.
  4. Once each app was properly laid out, a copy layer was made of each icon layer. These copies were rearranged to a more circular spatial pattern. All layer copies were merged into one then, using Scale Transform, this new layer was arranged to fit in the “Multiple Apps” section of the bubble chart.
  5. Created a copy of background overlay PNG and set the layer blending option to “linear burn.” This layer was set above all the icon layers to bolster the circle borders.
  6. Text was added using the Text Tool.