The Majority Illusion in Social Networks
June 09, 2015 ยท Declared Dead ยท ๐ PLoS ONE
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kristina Lerman, Xiaoran Yan, Xin-Zeng Wu
arXiv ID
1506.03022
Category
cs.SI: Social & Info Networks
Cross-listed
cs.CY,
physics.soc-ph
Citations
213
Venue
PLoS ONE
Last Checked
2 months ago
Abstract
Social behaviors are often contagious, spreading through a population as individuals imitate the decisions and choices of others. A variety of global phenomena, from innovation adoption to the emergence of social norms and political movements, arise as a result of people following a simple local rule, such as copy what others are doing. However, individuals often lack global knowledge of the behaviors of others and must estimate them from the observations of their friends' behaviors. In some cases, the structure of the underlying social network can dramatically skew an individual's local observations, making a behavior appear far more common locally than it is globally. We trace the origins of this phenomenon, which we call "the majority illusion," to the friendship paradox in social networks. As a result of this paradox, a behavior that is globally rare may be systematically overrepresented in the local neighborhoods of many people, i.e., among their friends. Thus, the "majority illusion" may facilitate the spread of social contagions in networks and also explain why systematic biases in social perceptions, for example, of risky behavior, arise. Using synthetic and real-world networks, we explore how the "majority illusion" depends on network structure and develop a statistical model to calculate its magnitude in a network.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Social & Info Networks
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
node2vec: Scalable Feature Learning for Networks
R.I.P.
๐ป
Ghosted
Cooperative Game Theory Approaches for Network Partitioning
R.I.P.
๐ป
Ghosted
From Louvain to Leiden: guaranteeing well-connected communities
R.I.P.
๐ป
Ghosted
Fake News Detection on Social Media: A Data Mining Perspective
R.I.P.
๐ป
Ghosted
Heterogeneous Graph Attention Network
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted