Attention inequality

This article is about User attention inequality. For Media attention inequality, see Media bias.

Attention inequality is the inequality of distribution of attention across users on social networks,^[1] people in general,^[2] and for scientific papers.^[3][4] Yun Family Foundation introduced "Attention Inequality Coefficient" as a measure of inequality in attention and arguments it by the close interconnection with wealth inequality.^[5]

Relationship to economic inequality

Attention inequality is related to economic inequality since attention is an economically scarce good.^[2][6] The same measures and concepts as in classical economy can be applied for attention economy. The relationship develops also beyond the conceptual level—considering the AIDA process, attention is the prerequisite for real monetary income on the Internet.^[7] On data of 2018,^[8] a significant relationship between likes and comments on Facebook to donations is proven for non-profit organizations.

Extent

As data of 2008 shows, 50% of the attention is concentrated on approximately 0.2% of all hostnames, and 80% on 5% of hostnames.^[6] The Gini coefficient of attention distribution lay in 2008 at over 0.921 for such commercial domains names as ac.jp and at 0.985 for .org-domains.

The Gini coefficient was measured on Twitter in 2016 for the number of followers as 0.9412, for the number of mentions as 0.9133, and for the number of retweets as 0.9034. For comparison, the world's income Gini coefficient was 0.68 in 2005 and 0.904 in 2018. More than 96% of all followers, 93% of the retweets, and 93% of all mentions are owned by 20% of Twitter.^[1]

Causes

At least for scientific papers, today's consensus states that inequality is unexplainable by variations of quality and individual talent.^[9][10][11] The Matthew effect plays a significant role in the emergence of attention inequality—those who already enjoy large amounts of attention get even more attention, and those who do not lose even more.^[12][13] Ranking algorithms based on relevance to the user have been found to alleviate the inequality of the number of posts across topics.^[7]

See also

• Attention economy
• Cumulative advantage
• Cumulative inequality theory
• Dominant narrative
• Doomscrolling
• Egotism
• Empathy gap
• Famous for being famous
• Filter bubble
• First World privilege
• Kardashian index
• Knowledge gap hypothesis
• Law of triviality
• Ortega hypothesis
• Overconsumption
• Pareto distribution
• Privilege hazard
• Rational expectations
• Social invisibility