Title
When the Crowd is Not Enough: Improving User Experience with Social Media through Automatic Quality Analysis.
Abstract
Social media gives voice to the people, but also opens the door to low-quality contributions, which degrade the experience for the majority of users. To address the latter issue, the prevailing solution is to rely on the ”wisdom of the crowds” to promote good content (e.g., via votes or ”like” buttons), or to downgrade bad content. Unfortunately, such crowd feedback may be sparse, subjective, and slow to accumulate. In this pa- per, we investigate the effects, on the users, of automatically filtering question-answering content, using a combination of syntactic, semantic, and social signals. Using this filtering, a large-scale experiment with real users was performed to mea- sure the resulting engagement and satisfaction. To our knowledge, this experiment represents the first reported large-scale user study of automatically curating social media content in real time. Our results show that automated quality filtering indeed improves user engagement, usually aligning with, and often outperforming, crowd-based quality judgments.
Year
DOI
Venue
2016
10.1145/2818048.2820022
CSCW
Keywords
Field
DocType
Automatic quality evaluation,Quantitative analysis,A/B testing,User engagement
User experience design,Social media,Computer science,User engagement,Downgrade,Filter (signal processing),A/B testing,Human–computer interaction,Multimedia,Syntax
Conference
Citations 
PageRank 
References 
1
0.34
32
Authors
5
Name
Order
Citations
PageRank
Dan Pelleg11552107.09
Oleg Rokhlenko225017.03
Idan Szpektor384159.44
Eugene Agichtein44549269.70
Ido Guy5144485.72