Can A User Anticipate What Her Followers Want?
Abir De, Adish Singla, Utkarsh Upadhyay, Manuel Gomez-Rodriguez

TL;DR
This paper investigates how social media users can predict and adapt to their followers' preferences by analyzing feedback, combining theoretical models with practical algorithms to understand and detect such behavior.
Contribution
It introduces a theoretical framework for understanding user feedback utilization and develops algorithms to identify this behavior in social media data.
Findings
Up to 82% of Twitter users use feedback to decide posts.
Up to 43% of Reddit users utilize feedback for posting decisions.
The proposed estimation framework accurately recovers users' utility functions.
Abstract
Whenever a social media user decides to share a story, she is typically pleased to receive likes, comments, shares, or, more generally, feedback from her followers. As a result, she may feel compelled to use the feedback she receives to (re-)estimate her followers' preferences and decides which stories to share next to receive more (positive) feedback. Under which conditions can she succeed? In this work, we first look into this problem from a theoretical perspective and then provide a set of practical algorithms to identify and characterize such behavior in social media. More specifically, we address the above problem from the viewpoint of sequential decision making and utility maximization. For a wide variety of utility functions, we first show that, to succeed, a user needs to actively trade off exploitation-- sharing stories which lead to more (positive) feedback--and exploration--…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Network Analysis Techniques · Spam and Phishing Detection · Opinion Dynamics and Social Influence
