Beautiful and Damned. Combined Effect of Content Quality and Social Ties on User Engagement

User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate and access pictures of varying beauty on Flic...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2017-12, Vol.29 (12), p.2682-2695
Main Authors: Aiello, Luca Maria, Schifanella, Rossano, Redi, Miriam, Svetlichnaya, Stacey, Liu, Frank, Osindero, Simon
Format: Article
Language:English
Subjects:
causal inference
Community participation
Computer vision
Content quality
Content-based retrieval
Flickr
image aesthetics
influence
Machine learning
matching
network effects
On-line systems
Pictures
Production
Quality
Recommender systems
Social network services
Social networks
User generated content
Visualization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate and access pictures of varying beauty on Flickr, we investigate how the production of quality impacts the dynamics of online social systems. We develop a deep learning computer vision model to score images according to their aesthetic value and we validate its output through crowdsourcing. By applying it to over 15 B Flickr photos, we study for the first time how image beauty is distributed over a large-scale social system. Beautiful images are evenly distributed in the network, although only a small core of people get social recognition for them. To study the impact of exposure to quality on user engagement, we set up matching experiments aimed at detecting causality from observational data. Exposure to beauty is double-edged: following people who produce high-quality content increases one's probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user's neighbors leads to a decline in engagement. Our analysis has practical implications for improving link recommender systems.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2017.2747552