Year: 2020
Type: article
Abstract: Social networks shape perceptions by exposing people to the actions and opinions of their peers. However, the perceived popularity of a trait or an opinion may be very different from its actual popularity. We attribute this perception bias to friendship paradox and identify conditions under which it appears. We validate the findings empirically using Twitter data. Within posts made by users in our sample, we identify topics that appear more often within users' social feeds than they do globally among all posts. We also present a polling algorithm that leverages the friendship paradox to obtain a statistically efficient estimate of a topic's global prevalence from biased individual perceptions. We characterize the polling estimate and validate it through synthetic polling experiments on Twitter data. Our paper elucidates the non-intuitive ways in which the structure of directed networks can distort perceptions and presents approaches to mitigate this bias.
Source: Nature Communications
Authors: Nazanin Alipourfard, Buddhika Nettasinghe, Andrés Abeliuk, Vikram Krishnamurthy, Kristina Lerman
Institution: Cornell University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $5,380
Awards: G1072749480, G1523888516, G2229863763, G260750634, G3392305090, G3848662913, TAS::57 3600::TAS 'DIGITAL DECEPTION: THE COGNITIVE AND SOCIAL MECHANISMS OF THE SPREAD OF FAKE NEWS', G657448715, G7452299184, G7947452623, MEASURING AND MITIGATING THE IMPACT OF NETWORK BIAS ON COMPUTATION IN GRAPHS
OpenAlex ID: W2944982573