Unpacking the Role of Gender(-ed speech) in Tech

The Super Bowl was great and with only the blink of an eye, the 2018 Winter Olympics are now under way. It has been four years since I was invited to write for the ACM (American Computing Machinery) to share thoughts for how female computer scientists can lead in the work place. I am intrigued by the most recent conversations in the tech world and even more specifically in Silicon Valley surrounding the role of women in the tech sphere. When Cultivate suggested that I use my data scientist role to share some recent insights on the topic, I gathered the tools required and got in sync with some of the team’s ongoing activities.

Cultivate is able to take an organization’s digital data, tease out important social dynamics, and reflect back to participating stakeholders valuable information about organizational engagement, inclusion, and wellness. In particular, understanding women’s responsibilities in the work environment is an essential issue in fostering a healthy workplace that is both functional and trustworthy.

Inspired by Cultivate’s technology and prior analysis revealing differences between male and female communication patterns, we’ll take a closer look at recent TechCrunch pitches under the same lens.

Talking Gender in the VC world

In 2017, the Academy of Management Journal published the result of a carefully designed experiment from Columbia University revisiting at an expert level the fact that women have different speech patterns than men, and that these speech patterns might actually impact who succeeds professionally. Specifically, the study forms a theory around “Promotion” (focused on hopes, achievements, advancement, and ideals) vs. “Prevention” (concerned with safety, responsibility, security, and vigilance) questions and answers.

Analyzing Q&A data from TechCrunch Disrupt New York between 2010 and 2016, the study asks blind participants to label questions and answers between entrepreneurs and venture capitalists as either “Promotion” or “Prevention”. The authors then go on to show how women are ultimately at a disadvantage when they provide “Prevention”-natured answers rather than “Promotion”-nature ones. This difference in behavior yields a significant difference in funding pattern:

“TechCrunch Disrupt entrepreneurs who were asked mostly prevention questions but gave mostly promotion responses went on to raise an average of $7.9 million in total funding. Conversely, those who responded to mostly promotion questions with mostly prevention answers went on to raise an average of only $563,000. An entrepreneur who is asked to defend her startups market share would be better served by framing her response around the size and growth potential of the overall pie than by merely stating how these plans to protect her share of the pie.”

“How Women Undermine Themselves With Words”

Women speak differently than men. Tracy Marchini and Deborah Cameron describe how women tend to use more qualifiers and tend to converse in a more exploratory manner. An article by Goop (a women’s lifestyle publication) suggests common words used by women that might cause them to come across as less confident or competent in the workplace: “How Women Undermine Themselves With Words”:

  • just — makes one come across as apologetic
  • actually — makes one appear surprised that they have a question or objection
  • qualifiers (such as “I’m no expert in this but …” and “I know you have been researching this for a long time but …”) — undermines one’s position before they’ve stated it

Wanting to understand if there was any support for these kinds of gendered speech patterns in an entrepreneurial setting, Cultivate analyzed four pitch transcripts from TechCrunch Disrupt SF 2017 using a combination of autosub, a transcription tool by Ager Manidis, and common shell scripting tools.

We analyzed and grouped the transcripts into two sets based on the gender of those leading the pitch (two male-led pitches and two female-led pitches), which we’ve anonymized as SetA and SetB. Then, using shell scripting tools wc, grep and gnuplot, Cultivate conducted a simple word analysis to see whether or not it was true that the women who pitched for Oneva and Future Family actually had a different distribution of commonly used words than the men who pitched for ColorMass and Pi.

Our initial analysis (using shell commands cat SetA.count | grep just and cat SetB.count | grep just, respectively) yielded 14 occurrences of the word just for SetA and 16 occurrences of the word just for SetB.

Continuing to reference the Goop article, we went on to check for occurrences of the words “actually” and “but”: finding 9 occurrences of the word actually for SetA and 13 occurrences of the word actually for SetB. Likewise, our analysis yielded 27 occurrences of the word but for SetA and 25 occurrences of the word but for SetB.

To better contextualize these initial numbers, we used gnuplot to figure out the actual distribution of commonly used words between the sets:

Most frequently used words in SetA, in order of frequency: (214 occurrences of the word the, 176 and, 165 to, 138 a, 121 we, 117 of, 116 that, 111 you, 88 in, 69 so, 67 is, 59 are, 58 i, 58 for, 54 your, 50 on, 42 it, 42 have, 40 with, 40 our, 38 can, 36 what, 34 this, 30 as, 29 really)

Visualized as a vertical bar graph below:

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Most frequently used words in SetB, in order of frequency: ( 212 occurrences of the word the, 136 to, 127 you, 107 and, 107 a, 104 of, 96 that, 82 we, 63 so, 62 in, 54 have, 53 it, 51 can, 50 is, 43 are, 40 for, 37 i, 34 what, 34 they, 33 this, 31 your, 31 now, 30 on, 29 how, 29 be)

Visualized as a vertical bar graph below:

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From this analysis, we found that there was no significant difference amongst the occurrences of the words just, actually and but between the two pitch sets. Rather, there was an apparent disparity in occurrences of the words we and you. Our distribution shows that SetA tends to use we (121 occurrences) more than you (88 occurrences) whereas SetB tends to use you (127 occurrences) more than we (82 occurrences).

Using this data, can you infer which set contains the female-led pitches and which contains the male-led pitches? Skip past these YouTube tips to find out.

Transcribing and Analyzing Text with YouTube

Interested in conducting your own text analysis with YouTube? Here are some of the tools and tricks we gathered while writing this post:

YouTube Tip #1: Using YouTube’s built-in transcription tool

To watch and select specific time segments within these videos, use the transcription feature built into YouTube.

YouTube has a built-in transcription tool that operates while the video is playing. In order to access it, click on the three dots (…) at the bottom right of the video. Then click on “open transcript” to see the scrolling content on the right. As a side note, I did a quick search through the transcripts to see whether words like “like” and “um” showed up more often in pitches featuring women at the forefront. I actually could not discern that these words showed up more often in these polished pitches.

YouTube Tip #2: Analyzing YouTube videos external to YouTube

I used Linux open source tools to download the video and run it through additional analysis. Alternatively, there are tools on the web, such as this one where you can download the .mp4 file. Once the video is downloaded, I prefer the use of vlc to play the videos back with closed captioning information. On YouTube, the files are saved as .vtt files but standard .srt files can also be used to playback txt.

YouTube Tip #3: Watching your video slowed down or sped up

Click on the gear icon aka the settings icon in the toolbar. In that menu, you should be able to see the third row with the option “speed”. By default, this is set to normal, but you can gear this down by a factor of 4 or up by a factor of 2 to watch content. This is useful when transcribing or attempting to analyze a phrase that isn’t immediately clear for content.

Shameless plug: you can watch Cultivate’s interview at TechCrunch Disrupt SF 2017 on YouTube here :).

The Proverbial “You”

… and now for the gendered breakdown of the pitch sets according to our simple word analysis at Cultivate:

The words in SetA were comprised of the transcripts from Oneva and Future Family (female-led pitches). The words in SetB were comprised of the transcripts from ColorMass and Pi (male-led pitches).

Thus, our results show that the female-led pitches tended to use the word we more frequently than the word you (about 37% more frequently, in fact) and the male-led pitches tended to use the word you more frequently than the word we (about 55% more frequently).

Although these results are not conclusive, they do suggest that speech patterns amongst male and female entrepreneurs might differ not only on in how they talk about themselves and their company (as suggested by Goop and the Colombia study), but also who they talk about when they do so.

About Cultivate

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