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Approval Without Amplification

Gender and visibility in algorithmically mediated entrepreneurship  ·  Katie Apker, Cornell ILR

The finding in 30 seconds

Women’s YouTube channels receive about 20 percent fewer views than men’s channels of the same age, video count, and content category. More positive comment tone correlates with fewer views, while more profanity correlates with more views, both across creators and within their own videos. Women’s channels receive more positive comments and about 55 percent less profanity, a pattern consistent with benevolent sexism. A Gelbach decomposition of the 20 percent gap at the same channel age, video count, and content category shows that comment tone accounts for 88.0 percent and profanity for 6.7 percent of the visibility gap, together 94.7 percent. The association between comment tone and views is the same for men’s and women’s videos within creators.

20%
fewer views for women's channels, comparing channels of the same age, video count, and content category
94.7%
of that 20% gap accounted for by comment tone and profanity (Gelbach decomposition)
9,096channels
450,000+videos
5.7M+audience comments
What explains it What does not The mechanism The talk The literature The paper

Decomposition of the Visibility Gap

Women’s channels receive about 20 percent fewer views than men’s when comparing channels of the same age, video count, and content category (9,096 channels). The Gelbach decomposition shows that 94.7 percent of this gap is associated with comment reception, mainly comment tone (88.0 percent) and profanity (6.7 percent), while creator content’s share of the 20 percent gap is minus 7.3 percent, meaning holding content equal widens the gap; other factors have no distinguishable shares.

Share of the 20% gap (same channel age, video count, and content category), percent. n.s. = not statistically distinguishable from zero.

  1. Creator sideBars show the share accounted for by creator-controlled content tone and profanity, which widens the gap, with channel trailer and HD video quality not distinguishable from zero.
    • Creator content: −7.3%
    • Channel trailer: +1.5% (n.s.)
    • HD video: +0.7% (n.s.)
  2. Amount of receptionBars show the share accounted for by comment volume, which is not statistically distinguishable from zero.
    • Comment volume: −0.2% (n.s.)
  3. Comment receptionBars show the share of the 20 percent gap accounted for by audience comment tone and profanity combined, accounting for 94.7 percent of the difference.
    • Comment tone: +88.0%
    • Comment profanity: +6.7%
  4. Not accounted forBars show the remaining share of the gap not accounted for by included factors; the full model’s gap is not statistically distinguishable from zero.
    • Left over: +10.5% (n.s.)

Each share represents the portion of the 20 percent gap accounted for by a factor when all factors are held equal together. Shares are an accounting of the change in the creator-gender coefficient between the model with creator gender, channel age, video count, and content category and the model that adds every block at once. Shares do not indicate causal effects. Negative shares mean holding that factor equal widens the gap. The order of factors does not affect shares.

In the raw 34 percent gap without controls, channel age and video count account for 56.4 percent of the gap, while content category’s share is minus 12.5 percent, meaning holding category equal widens the gap.

Alternative Explanations for View Gaps

Topic
Do women just pick lower-traffic topics?
No. In a Gelbach decomposition of the 19 percent gap, content category's share is minus 22.9 percent, widening the gap.
Comparing channels within the same 40 content categories the gap is about 20 percent, versus about 19 percent without category. Women’s categories average fewer views, but the gap remains significant within finer subtopics. Women’s channels still receive more positive and less profane comments within subtopics.
Output
Do women post fewer videos?
Partly. In a Gelbach decomposition of the raw 34 percent gap, channel age and video count account for 56.4 percent.
Women post fewer videos on average (834 versus 1,495), but the difference is not significant at the .05 level; the difference in logged videos per year of channel age is significant (p < .0001). At the same age, video count, and category, women’s channels still receive about 20 percent fewer views.
Titles and descriptions
Is it how women title and describe their videos?
No. Creator content accounts for minus 7.3 percent of the 20 percent gap in a Gelbach decomposition, widening it.
Women’s titles and descriptions are more positive, but this does not close the gap.
Transcripts
Is it what women say in their videos?
No. Including transcripts with titles and descriptions accounts for minus 9.7 percent of the raw 34 percent gap and minus 18.2 percent of the gap at the same channel age and video count in a Gelbach decomposition, widening the gap.
Women’s spoken content is more positive. Transcript missingness is not significantly different by gender.
Thumbnails
Is it how women package their videos?
No. None of the content elements accounts for the gap in models including all creator content and comment reception.
Women’s thumbnails show more positive facial expressions, warmer colors, and less clickbait.
Subscribers
Do audiences subscribe to women less?
No. Controlling for channel age and video count reduces the difference to 0.6 percent, not statistically distinguishable from zero.
Without controls, women’s channels have about 10 percent fewer subscribers.
Engagement
Do audiences engage less with women’s channels?
No. At the same channel age and video count, engagement per view is 24.7 percent higher and engagement per subscriber is 14.2 percent higher on women’s channels, both significant at the .05 level.
Women’s channels also receive more comments.
Who comments
Is it just male commenters?
No. Male-coded and female-coded commenters write 10.0 and 9.8 tone points more positively, respectively, and use less profanity on women’s channels, both significant at the .05 level.
Holding the share of female-coded commenters equal leaves a significant gap; the gap becomes not distinguishable from zero only when comment tone and profanity are included.
Self-promotion
Do women promote themselves less on other platforms?
No. Women’s channels link out more often (20.7 percent versus 16.4 percent, significant at the .05 level).
Holding linking equal leaves the gap unchanged, and linking’s association with views does not differ by gender.
Backlash
Are women penalized differently in female-typed categories?
No. The three-way interaction of gender, comment tone, and female-typed category is -0.04 and not statistically distinguishable from zero (p = .61).
The link between positive tone and fewer views is steeper in female-typed categories for both genders.

The 20 percent visibility gap and its association with comment tone and profanity hold under robustness checks including content-category clustered standard errors and wild cluster bootstrap, trimming extreme views, restricting to channels active at least twelve months, varying minimum comment floors from 20 to 500 per channel, weighting by comment count, holding like rate equal, and quantile regressions at the 25th, 50th, and 75th percentiles of views.

Positive Comments Associate with Fewer Views

Women’s channels receive more positive comments and less profanity, with commenters of both genders writing about 10 tone points more positively on women’s channels. At the same channel age, video count, comment volume, and content category, a one standard deviation increase in positive comment tone associates with about 35 percent fewer views, while a similar increase in profanity associates with about 5 percent more views. Within individual creators’ own videos, more positive comment tone is also associated with fewer views, and that association is the same on women’s and men’s videos. These associations form a negative arousal bonus. The suggested explanation, consistent with the data and prior research, is that recommendation systems and resharing audiences amplify visibility based on these engagement signals.

Women’s channels

  • More positive comments (tone 62.8)
  • Profanity 0.19 percent of comment words
  • More comments and higher engagement
This reception is associated with fewer views

Men’s channels

  • Less positive comments (tone 52.7)
  • Profanity 0.43 percent of comment words
  • Fewer comments and lower engagement
This reception is associated with more views
Diagram showing the suggested path from creator gender through the tone and profanity of audience comments, spread by recommendation systems and resharing audiences, to channel visibility.
Diagram showing the suggested path from creator gender through the tone and profanity of audience comments, spread by recommendation systems and resharing audiences, to channel visibility.

Talk Overview

This study asks how audience evaluation relates to entrepreneurial visibility on YouTube and whether these dynamics differ by gender. The analysis uses the 9,096 channels with comment data among creators drawn at random from the Infludata frame of U.S. individual creators with at least 2,000 subscribers. Creator gender was hand-coded, and the dataset includes over 5.7 million audience comments. I find that women’s channels receive about 20 percent fewer views than men’s when controlling for channel age, video count, and content category. More positive comment tone correlates with fewer views, and more profanity correlates with more views, both across and within creators’ videos. Women’s channels receive more positive comments and substantially less profanity, consistent with benevolent sexism. A Gelbach decomposition attributes 88.0 percent of the 20 percent gap to comment tone and 6.7 percent to profanity, together accounting for 94.7 percent, with women’s own content positivity widening the gap rather than reducing it.

What This Builds On

The finding rests on three established literatures: how gender shapes evaluation in entrepreneurship, how intense reactions spread online, and how favorable-seeming treatment can still be biased.

The evaluation and gender literature

  • Botelho & Poskanzer (2026), Administrative Science Quarterly
  • Abraham (2020), Administrative Science Quarterly
  • Kanze et al. (2018), Academy of Management Journal
  • Ewens & Townsend (2020), Journal of Financial Economics

Why intensity spreads and warmth does not

  • Berger & Milkman (2012), Journal of Marketing Research
  • Brady et al. (2017), PNAS
  • Rathje et al. (2021), PNAS
  • Milli et al. (2025), PNAS Nexus

Softer treatment as a form of bias

  • Glick & Fiske (2001), American Psychologist
  • Bareket & Fiske (2023), Psychological Bulletin
  • Nguyen et al. (2024), Entrepreneurship Theory & Practice

Full references are in the manuscript.

Read It in Full

Women’s channels receive about 20 percent fewer views than men’s when comparing channels of the same age, video count, and content category. In a Gelbach decomposition of the 20 percent gap, comment tone and profanity together account for 94.7 percent. Women’s channels receive more positive and less profane comments from commenters of both genders, and engagement per view is about 25 percent higher on women’s channels. Subscriber counts are not distinguishable from men’s when controlling for channel age and video count. At the same channel age, video count, comment volume, and content category, a one standard deviation increase in positive comment tone associates with about 35 percent fewer views, while increased profanity associates with about 5 percent more views. These patterns align with prior findings that recommendation systems and audiences favor negative, high-arousal reactions. Women’s own content is more positive than men’s and does not account for the visibility gap. The manuscript is available on request.

Plot of the creator-gender coefficient across six nested models tracks the coefficient with creator gender alone; plus channel age and video count; plus creator content; plus comment volume; plus comment profanity; and plus comment tone, shown with and without content-category indicators. The gap is not statistically distinguishable from zero once comment tone enters.
Plot of the creator-gender coefficient across six nested models tracks the coefficient with creator gender alone; plus channel age and video count; plus creator content; plus comment volume; plus comment profanity; and plus comment tone, shown with and without content-category indicators. The gap is not statistically distinguishable from zero once comment tone enters.

Cite this paper

@unpublished{apker_approval_2026, author = {Apker, Katie}, title = {Approval Without Amplification: Gender and Visibility in Algorithmically Mediated Entrepreneurship}, note = {Working paper, Cornell University, ILR School}, year = {2026} }