Many view social media as a great equalizer that gives everyone the opportunity to be heard.

But a new study from Columbia University has shown that women may be more likely to be drowned out on popular social media platforms like Instagram.

Social networks have been known to be a breeding ground for 'homophily', wherein people have the impulse to connect with other users who are just like them.

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Many view social media as a great equalizer that gives everyone the opportunity to be heard. But a new study from Columbia University has shown that women may be more likely to be drowned out on popular social media platforms like Instagram. Stock image

This became apparent as the researchers studied how people interact on Instagram.

They found that men were 1.2 times more likely to like or comment on other male users' photos than women's, while women were 1.1 times more likely to like or comment on other women's photos.

And, popular social recommendation algorithms like 'Who to Follow' on Twitter, 'People you may know' on Facebook and 'Suggested accounts' on Instagram enhance the homophily experienced on social media.

The result is an 'algorithmic glass ceiling' that is similar to real social barriers, 'hindering groups like women or people of color from attaining equal representation,' according to the study.

'We are simply showing how certain algorithms pick up patterns in the data,' said Ana-Andreea Stoica, the study's lead author, in a statement.

'This becomes a problem when information spreading through the network is a job ad or other opportunity'

'Algorithms may put women at an even greater disadvantage,' she added.

Pictured is a selfie from study author Ana-Andreea Stoica's Instagram account. She and other researchers from Columbia University found that men were more likely to receive engagement on posts than women on Instagram, creating an 'algorithmic glass ceiling'

Of the 550,000 surveyed users, 52% of men received at least 10 likes or comments compared to 48% of women. This disparity became even more pronounced as a result of algorithms

Women's posts receive incrementally less engagement on Instagram despite the fact that females made up the majority of the surveyed users, according to the researchers.

Of the 550,000 surveyed users, 52% of men received at least 10 likes or comments compared to 48% of women.

This disparity became even more pronounced after algorithms were introduced.

When the researchers used popular algorithms like Adamic-Adar and Random Walk, they found that the percentage of women predicted to be recommended to at least 10 other Instagram users fell to 30%, the study said.

They found that the gender disparity was greatest in Instagram's 'super-influencers', or people in the top .1 percent for engagement.

When researchers used popular algorithms, they found that the number of women predicted to be recommended to at least 10 other Instagram users fell to 30%

IBM, MICROSOFT SHOWN TO HAVE RACIST AND SEXIST AI SYSTEMS In a 2018 study titled Gender Shades, a team of researchers discovered that popular facial recognition services from Microsoft, IBM and Face++ can discriminate based on gender and race. The data set was made up of 1,270 photos of parliamentarians from three African nations and three Nordic countries where women held positions. The faces were selected to represent a broad range of human skin tones, using a labelling system developed by dermatologists, called the Fitzpatrick scale. All three services worked better on white, male faces and had the highest error rates on dark-skinned males and females. Microsoft was unable to detect darker-skinned females 21% of the time, while IBM and Face++ wouldn't work on darker-skinned females in roughly 35% of cases. In a 2018 study titled Gender Shades, a team of researchers discovered that popular facial recognition services from Microsoft, IBM and Face++ can discriminate based on gender and race Advertisement

Researchers say algorithms further 'prevent females from rising to the most commented and liked profiles', noting that there's a 'drastic gap' between genders.

'But under which conditions can we ensure that those suggestions are not reproducing, or worse, reinforcing our historical biases, combined with a cloudy illusion of objectivity?', the study continued.

Tech giants should design future social recommendation algorithms that value relevance, while also taking care not to create a glass ceiling of sorts, the researchers said.

'Algorithms pick up subtle patterns and amplify them,' said Augustin Chaintreau, the study's senior author, in a statement.

'We're not asking that algorithms be blind to the data, just that they correct their own tendency to magnify the bias already there'.