Research & Insights

By Renette Youssef, November 6, 2014

He Tweeted, She Tweeted: A Study on Romantic Breakups on Twitter Using Data Science

 

They say that breaking up is hard to do. Now, data scientists know that it’s true. Neil Sedaka songs aside, they know it’s true because, in 2013, with the help of public data sourced from Twitter, they were able to track and listen-in on conversations between 661 couples who were in the process of ending their relationships.

Researchers Venkata Rama Kiran Garimella, Ingmar Weber, and Sonya Dal Cin published the results of their study in this paper: From “I Love You Babe” to “Leave me alone” – Romantic Breakups on Twitter.

Before looking at the findings of the study, there are a few interesting details to consider:

  1. The study excluded married couples and other non-typical couples
  2. The researchers paid close attention to the many psychological clues gleaned from the digital environment (that is Twitter) surrounding the breakup
  3. The researchers used a number of tools to parse the data, including tools from social-data company GNIP and our data-enrichment platform CrowdFlower.

Surprising Findings:

  1. Breakups often resulted in a batch unfollowing: an average 15-20 users

  2. Maybe not so surprising, but couples who breakup tend to be fresher (i.e., crass, brazen, or rude) when compared to couples that were still together

  3. There were higher levels of post-breakup Twitter communication between couples who had high pre-breakup levels of interaction

  4. 67% of the time, women were the rejector in the relationship

  5. After a breakup, rejectees became more self-centered. They tried to find stability in religion and spirituality, yet they also tended to publicly curse their luck, life, and fate

  6. A significant portion of the users made their profiles private after a breakup
  7. There was a surprising amount of public fighting.

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Not So Surprising Findings:

 

  1. The longer the couple was together during the study, the less likely they were to breakupbreakuptwitter1.png

  2. ‘Stonewalling’ (i.e., ignoring the other’s messages) was a sign of an impending breakup (38% of the time)

  3. Right before a breakup, there was a decrease in the fraction of messages sent to the partner, and an increase in the fraction of messages to other users

  4. There was increased use of “depressed” terms after the breakup compared to the use from couples who did not breakup

  5. Rejectees were a lot more likely to use depressed terms compared to rejectors. This was true both before and after the breakup.

Lessons to be Learned?

The study notes, 85% of people will experience a breakup at some point in their life. Maybe in the future, when digital assistants become commonplace, they will pick up on these warning signs and remind us to send flowers and clean up our act at just the right moment! The researchers suggested creating this idea as a kind of early break-up warning system.

Another lesson to be learned is that when it comes to data science, nothing is sacred. The information locked behind our everyday interactions online might be revealing more about us, and human nature, than we could have ever guessed.

Some of the reasons for using Twitter, cited in the paper include:

  1. Data collection was relatively easy, especially when facilitated by platforms like CrowdFlower

  2. The size of data was impressive, and there was a lot to work with

  3. Since users did not know they were being watched, there was less self-reporting bias

  4. The platforms allowed for timely collection of data around the moment of break-up

  5. Having social context in the form of network information.

 

Some of the Challenges:

  1. The noise of data made it difficult to navigate

  2. The lack of well-defined variables made it a challenge to define the experiment

  3. There were difficulties in observing other psychological variables

  4. There was limited power for determining causal links

  5. Privacy concerns had to be carefully considered.

Overall, it’s amazing to see what you can do with a chunk of data mined from a public datasource like Twitter. Are you a data geek? What do you think you would do with this data? Let us know in the comments.