Paper Info
Title
Followee Management: Helping Users Follow the Right Users on Online Social Media.
Abstract
User timelines in Online Social Media (OSM) remains filled with a significant amount of information received from followees. Given that content posted by followee is not under user's control, this information may not always be relevant. If there is large presence of not so relevant content, then a user may end up overlooking relevant content, which is undesirable. To address this issue, in the first part of our work, we propose suitable metrics to characterize the user-followee relationship. We find that most of the users choose their followees primarily due to the content that they post (content-conscious behavior, measured by content similarity scores). For a small number of followees, a high degree of social engagement (likes and shares) irrespective of the content posted by them is observed (user-conscious behavior, measured by user affinity scores). We evaluate our proposed approach on 26,516 followees across 100 random users on Twitter who have cumulatively posted 234,403 tweets. We find that on average for 60% of their followees, users exhibit very low degree of content similarity and social engagement. These findings motivate the second part of our work, where we develop a Followee Management Nudge (FMN) through a browser extension (plugin) that helps users remain more informed about their relationship with each of their followees. In particular, the FMN nudges a user with a list of followees with whom they have least (or never) engaged in the past and also exhibit very low similarity in terms of content, thereby helping a user to make an informed decision (say by unfollowing some of these followees). Results from a preliminary controlled lab study show that 62.5% of participants find the nudge to be quite useful.
Year
DOI
Venue
2018
10.5555/3382225.3382483
ASONAM '18: International Conference on Advances in Social Networks Analysis and Mining Barcelona Spain August, 2018
Keywords
Field
DocType
Online Social Media, Profile Management, Recommendation System, Nudge Design
Recommender system,World Wide Web,Profile management,Social media,Computer science,Nudge theory,Timeline,Artificial intelligence,Plug-in,Social engagement,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-5386-6051-5
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Anjali Verma100.34
Ashima Wadhwa200.34
Navya Singh300.34
Shivangi Beniwal400.34
Rishabh Kaushal501.35
Ponnurangam Kumaraguru619216.59