Paper Info
Title
Purple Feed: Identifying High Consensus News Posts On Social Media
Abstract
Although diverse news stories are actively posted on social media, readers often focus on the news which reinforces their pre-existing views, leading to 'filter bubble' effects. To combat this, some recent systems expose and nudge readers toward stories with different points of view. One example is the Wall Street Journal's 'Blue Feed, Red Feed' system, which presents posts from biased publishers on each side of a topic. However, these systems have had limited success. We present a complementary approach which identifies high consensus 'purple' posts that generate similar reactions from both 'blue' and 'red' readers. We define and operationalize consensus for news posts on Twitter in the context of US politics. We show that high consensus posts can be identified and discuss their empirical properties. We present a method for automatically identifying high and low consensus news posts on Twitter, which can work at scale across many publishers. To do this, we propose a novel category of audience leaning based features, which we show are well suited to this task. Finally, we present our 'Purple Feed' system which highlights high consensus posts from publishers on both sides of the political spectrum.
Year
DOI
Venue
2018
10.1145/3278721.3278761
PROCEEDINGS OF THE 2018 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY (AIES'18)
Keywords
Field
DocType
Consensus, News consumption in social media, Polarization, Audience leaning based features, Purple feed
Filter bubble,Social media,Media studies,Computer science,Artificial intelligence,Political spectrum,Operationalization,Politics,Machine learning
Conference
Citations 
PageRank 
References 
1
0.35
10
Authors
6
Name
Order
Citations
PageRank
Mahmoudreza Babaei1275.41
Juhi Kulshrestha21066.61
Abhijnan Chakraborty312116.25
Fabricio Benevenuto42840155.33
P. Krishna Gummadi57961511.50
Adrian Weller614127.59