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Friendship paradox biases perceptions in directed networks
Nazanin Alipourfard1, Buddhika Nettasinghe2, Andrés Abeliuk3
1Information Sciences Institute, 4676 Admiralty Way, Marina Del Rey, Los Angeles, CA, 90292, USA. nazanina@isi.edu.
Nature Communications
|February 7, 2020
Summary
Social networks create perception bias due to the friendship paradox, where popular opinions seem more common than they are. This study identifies bias conditions and offers a polling algorithm to estimate true popularity from skewed social media data.
Area of Science:
- Social Network Analysis
- Computational Social Science
- Information Science
Background:
- Social networks influence individual perceptions of trait and opinion popularity.
- Perceived popularity often deviates from actual global prevalence due to network structures.
- The friendship paradox describes how individuals in a network tend to have more connections than the average connection.
Purpose of the Study:
- To investigate the perception bias in social networks stemming from the friendship paradox.
- To identify the conditions under which this perception bias emerges.
- To develop and validate a method for estimating true global prevalence from biased individual perceptions.
Main Methods:
- Empirical validation using Twitter data to identify topics overrepresented in user feeds compared to global posts.
- Development of a polling algorithm that utilizes the friendship paradox for efficient prevalence estimation.
- Synthetic polling experiments on Twitter data to validate the polling algorithm's accuracy.
Main Results:
- Social network structures, specifically the friendship paradox, demonstrably distort perceptions of popularity.
- Topics appearing more frequently in individual social feeds than globally highlight this perception bias.
- The proposed polling algorithm provides a statistically efficient estimate of global prevalence from biased perceptions.
Conclusions:
- Directed network structures can lead to non-intuitive distortions in user perceptions.
- Understanding and mitigating perception bias is crucial for accurate social data analysis.
- The developed polling algorithm offers a practical approach to correct for network-induced biases in prevalence estimation.
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