Big question: Is LinkedIn quietly diminishing women’s voices?
What a difference six months makes. This is an update of a post I wrote in March when I was asked the question in a workshop I ran at The Nine, Brussels with colleague Robert Baker of Potentia Consulting. whether I thought LinkedIn showed gender bias
At the time I felt that it was more about the culture and the use of the platform by its members which reflected the gender split (57% men to 43% women)
To fully understand this second post you MUST read Part 1
Context
Since then, there has been much discussion about the way the LinkedIn algorithm has changed and dismay has been voiced at the reach all members achieve when they publish posts. A couple of years ago I could post some meaningful content and regularly get five figure exposure. This is no longer the case, and I assumed every one was impacted in the same way.
Based on a growing and vociferous campaign initiated by the indomitable Cindy Gallop Founder of MakeLoveNotPorn, around the lack of reach of her posts, Jane Evans, Founder The 7th Tribe kicked off a small, rudimentary, but what would turn out to be telling experiment at the end of this summer. The aim was to to see if we could get some useful data around gender differences in reach achieved by content posted on LinkedIn. Another question was whether we could find an explanation as to why they those results might be different. This is not intended as deep scientific research but the next step to anecdotal commentary and the basis for further discussion.
Bottom line question – is code playing a role? Look at the data and decide for yourself.
The experiment
Four LinkedIn members, two women and two men, posted identical content at the same time to measure how far their posts would reach relative to their followings. The participants were:
- Cindy Gallop : US entrepreneur (137,000+ followers)
- Jane Evans: UK entrepreneur and activist (17,000+ followers)
- Matt Lawton; Australian CMO (8,661 followers)
- Stephen McGinnis: US West Coast Creative Director (728 followers)
The results were significant:
- Cindy Gallop’s post: 801 views (0.6% of followers)
- Jane Evans’s post: 1,327 views (8.3%)
- Matt Lawton’s post: 10,409 views (143%)
- Stephen McGinnis’s post: 328 views (51%)
Although Cindy Gallop’s has the largest network, her reach was the lowest in terms of the proportion of her followers. Interestingly, the two male participants who had far fewer followers succeeded reaching a larger share of their respective networks.
Women have been complaining about low visibility on LinkedIn for some time, but this experiment despite being a small sample produced interesting data worth examining and should not be ignored.
My own experiment
I participated in the experiment organised by Jane Evans, pairing with Robert Baker when we posted the exact same content at the exact same time, on the exact same day. I wrote the content.
See below the results
I mentally tossed around a number of possibilities for this: the topic of Male Allies is more Robert’s field of interest, hence the discrepancy. Was legitimacy bias kicking in where higher value is placed on the voices of men in many situations, which is at the root of the authority gap? I call this “daily dimming” when it happens routinely, where women’s contribution is over-looked, undervalued, or unrecognised. This is low level form of daily sexism but with big impact. Or maybe my followers are not interested in Male Allies.
But even so the results are clear. Those screen shots were taken on 22nd October 2025
Further study
There is no published research to confirm that LinkedIn deliberately suppresses content written by women. My own observations that posts on sexism and harassment get much lower numbers, so much so, that I (and others) have even started using asterisks to get round it. That my confuse the algos even further I am told by experts. But these numbers do raise some valid questions:
1. How does the distribution of content work? Will we ever know how the algorithm works? Tony Restell a UK based LinkedIn Expert posted a really helpful video which sheds at least some light on a very opaque issue.
2. Shadow banning: Can we conclude that certain subjects are shadow banned? For the uninitiated shadow banning is often described as a “silent” form of suppression: content isn’t removed, but it’s quietly made less visible.
There is a list of banned words from the US Federal Administration which includes lots of the topics I write about. DEI, sexism, harassment, women, “women in leadership”, trauma, bias etc. There are 350 words in total. When applied unevenly, even unintentionally, shadow banning can reinforce existing structural inequities in professional spaces.
3. Content type: My own most popular post in recent months was a piece of fluff about a connection request from a fake profile of Tom Hiddleston, which garnered nearly 20000 impressions. Now Tom H is always good news, but even so. Circulating a survey on sexism and harassment realised FOUR impressions. So maybe it’s not me personally, because when I post nonsense, I get traction. Is there some underlying mechanism that nudges women into their “own lanes” of lighter content and the serious content is not given the same weight as content delivered by a man?
4. Network Growth : LinkedIn offer us all when we log in a list of suggested content. How does that work? Is it gender balanced and related to your field of interest? How is the “you might also know section” configured – is that gender balanced? I haven’t paid attention but will start. Are women being algrorithmically and subtly directed to network with other with other women which tends to be lower power as women hold lower positions in business hierarchies.
5. Network composition: If women’s networks are composed of greater numbers of women does that mean our content is treated less seriously in the same way off line where women gossip and men network? Is that supported in any way by the algos?
Equal opportunity
Platforms such as LinkedIn play an increasingly central role in professional networking and visibility raising. When women’s content fails to get traction this impacts all areas of their careers. Business owners and their revenue potential, job seekers and their job search prospects and the general benefits of being a continuous and visible contributor.
It’s critical to ensure that legitimate topics receive the same treatment from the algorithms, whether posted by men, women or other genders. To ignore this a form of second-hand sexism which denies access, representation, and influence as well as business and career opportunities, all of which can have significant impact on women’s professional well being.
What is important is the algorithms of these platforms are audited for proxy bias, to ensure that they are not embedding pre-existing cultural biases and amplifying them to scale and at speed.
The data may be small, but the message is loud. Women’s voices deserve to be heard, equally and visibly. If you want to navigate hidden career barriers in time of uncertainty
