Grace Woo
Grace Woo
Mar 12, 2018 · 5 min read

In my previous article with Cultivate, we used a variety readily available transcription and common shell scripting tools to analyze speech patterns in TechCrunch pitch competitions.

Inspired by a study out of Columbia University, which found differences in the types of questions posed to men and women in a pitch competition setting, we also decided to conduct our analysis through the lens of gender. Our most interesting finding: in the four TechCrunch pitches we analyzed, we observed that women tend to use the word we more often in their pitches than men do.

As next step we decided to look at what we-focused airtime could mean from a data analytics perspective — this time by looking at how airtime is shared between team members.

We referred to an article in the New York Times documenting Google’s well-known “Project Aristotle”: a project analyzing why some work teams thrive and others struggle. As study of team dynamics and productivity, there are many takeaways from this project in reference to verbal airtime.

To paraphrase the high-density article: “good” teams can look very different from one another in terms of team makeup, but share certain behavioral commonalities. Namely, team members speak roughly the same amount in meetings, and the group has a high emotional sensitivity to its members.

We were interested to see whether it was possible to take a data-driven approach to the former trait, which researchers in the article refer to as “equality in distribution of conversational turn-taking.” Again we decided to use publicly available TechCrunch meetings and conversations as our data set.

Calculating Airtime

One limitation of the free transcription tools available through YouTube is the ability to distinguish different speakers. For example, in the previous post, we were not able to automatically distinguish between content spoken by the person giving the pitch and questions asked by the judges. To close this gap, we wrote a simple Python tool which annotates the transcripts output by open source tools (specifically, the .json transcript files written out using Ager Manidis transcription tools, which contain the start time, end time, and content of each statement).

Our human-assisted script asks for additional hand-entered annotation for who is actually speaking. After I went in and hand annotated each one of these segments, it became possible to distinguish each individual participant in the group conversation.

Next, I wrote another Python script to both record the name of each person speaking and tally up the total amount of airtime for each participant.

For this blog post, I chose to try out our tools on a TechCrunch interview moderated by technology journalist Leena Rao and featuring Brian Lee and Jessica Alba of The Honest Company.

Here’s a visualization of the results — a histogram showing seconds of airtime for each of the three interview participants:

The actual airtime for Leena Rao, Jessica Alba, and Brian Lee was 216.094, 517.888, and 555.264 seconds respectively, in a clip that was 1670 seconds long (approx. 27 minutes). The clip also contains about 382 seconds (approx. 6 minutes) of non-speaking time.

Going off Project Aristotle’s findings, we would expect a similar share of airtime between the co-founders to be indicative of a more productive team. In this interview, that appears to be the case, with co-founders Jessica Alba and Brian Lee speaking a similar 517.888 and 555.264 seconds each respectively.

What role might a discussion moderator (or perhaps, a manager) play in delegating equal airtime? In her interview with Alba and Lee, moderator Leena Rao delegates most of airtime to the two participants, speaking the least at 216.094 seconds of airtime.

How did Leena use that airtime to lead the discussion? After hand annotating each person’s role in the transcript file, we were able to see that Leena’s well spaced questions over time are likely what was able to drive a balanced conversation:

Facilitating Balanced Participation

As a manager or a discussion leader, balancing airtime can come in many different forms. In her Forbes article “5 Simple Things You Can Do To Get People To Speak Up In Meetings,” business author Erika Andersen cites five qualitative suggestions for leaders looking to boost team participation in discussions:

  • Give people lead time — If you’re planning to bring up a new topic or conversation in a meeting, let the team know in advance.
  • Frame it up — Be specific when you ask questions. Your team needs context to be able to voice opinions usefully.
  • Stop talking — Let your team get a word in. Your team can’t voice their opinion if you’re taking all of the airtime.
  • Take it in — If you shoot down a team member’s idea, when possible tell them what you liked about the idea before you raise your concerns. This will encourage them to speak again in the future.
  • Use what you hear — Reward your team for speaking by putting their ideas into action and giving them credit.

Looking at Leena’s participation timeline alone, we can infer that she is employing at least two of Andersen’s suggested strategies for facilitating discussion.

Her comments are evenly spaced and give Alba and Lee time to contribute. She also spends significantly more time listening and processing the content she hears in comparison to Jessica and Brian, taking up right around 20% of the total airtime. That’s some pretty smooth driving!

However, verbal airtime is just one dimension of a discussion, and likely only a coarse indicator of a healthy team and a strong discussion facilitator. Diving into the actual content of the conversation or exploring other communication modes such as e-mail could in the future provide a more fine-grained understanding of how teams with well-balanced airtime operate, and how to encourage that behavior.

About Cultivate

Cultivate’s AI-powered platform enables engagement and inclusion by helping you understand and improve your workplace communications.

For more information on what we are doing at Cultivate, check out our website.

Cultivate AI

Sharing interesting findings about people analytics, employee engagement, inclusion, and language processing, as we build AI around workplace communication. More info at

Grace Woo

Written by

Grace Woo

Writes on Medium for @CultivateAI |

Cultivate AI

Sharing interesting findings about people analytics, employee engagement, inclusion, and language processing, as we build AI around workplace communication. More info at

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