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]
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.
The attention economy is a marketplace that exists online and in the physical world in which companies, social media platforms, news outlets, apps, and more compete for the attention of people. Human attention is highly sought after because it can be directly turned into profit, through advertising, data, and influence. It’s difficult to grasp because humans only have a limited number of hours in a day, and our brains are only capable of focusing on so much within those few hours. So, companies and platforms compete for attention – they try to hold users’ attention for as long as possible, utilizing highly developed algorithms to capture and maintain attention, aiming to secure the most engagement possible out of every user before they move on to the next stimulus.
In social media, attention inequality refers to the uneven distribution of users’ attention on social media platforms. This means that instead of an equal distribution of attention, fewer sources receive a disproportionate share of attention, leaving many to be drowned out. This phenomenon is largely the result of the algorithms that control the content that is viewed by a platform’s users, which is designed, as mentioned in the previous section on the attention economy, to drive maximum engagement.
This phenomenon is a large factor in the polarization and creation of so-called “echo-chambers” that exist in social media today. Algorithms designed to maximize engagement tend to reward content that is already performing well and pump it out to more users, while content that is equally engaging or well-made is not recommended to users, and thus is only seen by very few people. Because of the design of social media platforms, posts that trigger more extreme emotions (anger, sadness, awe) out-perform content that is less extreme or controversial. When many people fall into this trap and offer the outrage or emotional reaction that the video aims to elicit, their engagement (an angry comment or a share to a friend, for example) signals to the algorithm that the specific post drives engagement. The algorithm then recommends this post to an exponential amount of people, whose outrage or awe fuels this cycle, rewarding extreme content while burying content that is truthful or sincere.
These factors, when combined, create a social media environment that is dominated by the few and driven by the behavior of the many. The people that take advantage of this reality and have a firm grasp on the technology necessary to edit and publish high-quality videos very frequently, tend to succeed in this ecosystem. Others, no matter how thoughtful or talented, often end up with far less attention than those catering to the algorithm.
According to a recent 2025 study about research inequality among scientists published in Information Processing and Management, researchers discovered that much like in social media, scientific discourse is largely restricted to a small group of highly connected scientists, and is not an accurate representation of the whole scientific community.
Using citation-network analysis in the fields of nanoscience and chemical physics, the researchers discovered that an elite group of well-connected scientists dominates the information that is consumed and shared in the scientific community. The calculated connection strength between these scientists reaches about 4.5, meaning that these few elite authors cite each other four times more often than would be predicted in a random network, whereas “ordinary” scientists that exist outside of this group only reach a connection strength of 0.9. This means that ordinary, fully-qualified scientists are not only overlooked by the elite group of scientists, but are actually underconnected with each other.
These findings highlight a fundamental inequality in the scientific community, demonstrating that scientific attention is not distributed by merit, but rather by the connectedness of the scientists involved in the research. Tightly-knit elite citation circles benefit those who exist within the connected circle, while failing to pay attention to peripheral researchers, no matter how relevant their work may be.
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]
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]