The Interactive Relationship Between Echo Chamber on the Internet and Gender Equality

Yi Yao

doi:10.54254/2753-7048/2025.NE26175

Lecture Notes in Education Psychology and Public MediaOpen access

The Interactive Relationship Between Echo Chamber on the Internet and Gender Equality

Research Article

Open Access

The Interactive Relationship Between Echo Chamber on the Internet and Gender Equality

Yi Yao ^1*

¹ Jiaxing Senior High School

^*Corresponding author: 2592518168@qq.com

Published on 19 August 2025

LNEP Vol.116

ISSN (Print): 2753-7056

ISSN (Online): 2753-7048

ISBN (Print): 978-1-80590-331-4

ISBN (Online): 978-1-80590-332-1

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Abstract

With the widespread use of social media, algorithms play an increasingly significant role in information dissemination, potentially reinforcing the echo chamber effect. At the same time, the internet has seen a surge in bloggers discussing gender equality issues from feminism to men's rights activism. Yet, echo chambers may funnel users toward increasingly extreme content, potentially fueling gender biases and even deepening societal divisions. As a consequence, this study examines if social media echo chambers increase gender inequality through algorithmic recommendations. The study combined experiments and interviews. Researchers created 16 controlled Douyin (Chinese TikTok’s counterpart) accounts divided into male and female groups that either searched for gender equality content or used the platform normally. Analysis of 800 videos (50 per account) showed active accounts got 258 gender equality videos versus 52 for normal accounts. Normal female accounts received 84% lifestyle content, while normal male accounts got 72% entertainment and materialistic content. Additional analysis and interviews tracked attitude changes to measure echo chamber effects. The findings show algorithms may reinforce gender biases through selective content exposure. This study shows algorithms strengthen gender stereotypes via feedback loops, increasing social division. Key findings reveal algorithms filter content by gender, boost extreme views through engagement rankings, and create separate information bubbles that worsen group misunderstandings. Interviews showed active-search users became more aware of algorithmic bias, increasing by 2.1 points, while normal users increased by 1.3 points. The findings help improve platform recommendations and encourage users to explore diverse content.

Keywords:

Echo Chamber, Social Media, Gender Inequality, Algorithmic recommendations, Gender Equality

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