What is it about?

Understanding how we see ourselves within our social circles is more complicated than it seems. Although people interact daily with classmates, colleagues, and friends, our research shows that we have surprisingly limited awareness of our own popularity and influence within these networks. Using experimental data from 11 university classes, we mapped strong-tie friendship networks and then asked students to estimate both their popularity and their centrality, as well as to identify the most popular and central individuals in their class. Participants received monetary incentives for their predictions: the higher the accuracy, the higher the reward. The results reveal striking patterns of misperception. Only about one in four students correctly guessed how many classmates considered them a friend — that is, their actual number of strong-tie connections — and fewer than 10% accurately identified their own centrality decile in the network. Even more surprising, students’ perceived centrality showed almost no correlation with their true centrality (ρ < 0.1), suggesting that people are essentially blind to their structural importance in the group. While students had slightly better intuition about popularity, both distributions of perceived popularity and perceived centrality were far more compressed than the real ones, meaning people tend to imagine social worlds that are flatter and less hierarchical than they actually are. Importantly, these misperceptions matter. Students who viewed themselves as less popular or less central, and those who estimated their position more accurately, tended to earn higher academic grades, even after controlling for actual network position. Likewise, students frequently recognized by others as popular or central achieved significantly better academic outcomes. Altogether, these findings reveal a social mirage: a systematic gap between how influential we think we are and how influential we actually are. This unawareness not only distorts our understanding of social life but also shapes important real-world outcomes.

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Why is it important?

Example 1: Public health risks When people are unaware of how central they truly are in a social network, they misjudge both their own risk of infection and the risk they pose to others. Highly connected individuals may underestimate their role in spreading contagious diseases, reducing compliance with targeted interventions and weakening behavioral responses. This mismatch between real and perceived centrality can amplify outbreaks, especially when superspreaders do not recognize themselves as such. Example 2: Gossip and unintended harm Individuals who occupy central positions but fail to recognize it may unintentionally amplify negative information. A comment made by someone who is more influential than they realize can travel farther and faster, causing greater social damage than expected. Misperceptions of one’s influence therefore make harmful gossip more potent, as people do not calibrate the impact of their words to their true network reach. Example 3: Limits of network-based economic theory Many network games assume that individuals know exactly where they are located in the network, who their neighbors are, and how others depend on them. Our findings challenge this assumption: if people cannot accurately assess their own popularity or centrality, they cannot optimize utilities based on a network they do not fully perceive. This creates a disconnect between theoretical predictions and real-world behavior, calling for models built on perceived—not perfect—networks.

Perspectives

This project made us realize how little we really understand our place in social groups. We were surprised to see how often people misread their own importance. These mistakes influence how we behave, how we connect with others, and how we see ourselves. For us, the main message is simple: our social influence is not only unequal, but also widely misunderstood.

Pablo Branas-Garza
Universidad Loyola Andalucia

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This page is a summary of: Perception of own centrality in social networks, Proceedings of the National Academy of Sciences, November 2025, Proceedings of the National Academy of Sciences,
DOI: 10.1073/pnas.2420334122.
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