Scheduling Broadcasts in a Network of Timelines

TL;DR

This paper introduces a formal model for scheduling social media broadcasts that maximizes attention by considering user behavior phenomena like overload and rhythms, validated through Twitter data.

Contribution

It is the first to formalize the broadcast scheduling problem incorporating behavioral phenomena and to propose an effective scheduling strategy based on real data.

Findings

Discovered a counter-intuitive scheduling strategy that outperforms common heuristics.

Validated the model using real Twitter data.

Produced fewer posts while increasing attention.

Abstract

Broadcasts and timelines are the primary mechanism of information exchange in online social platforms today. Services like Facebook, Twitter and Instagram have enabled ordinary people to reach large audiences spanning cultures and countries, while their massive popularity has created increasingly competitive marketplaces of attention. Timing broadcasts to capture the attention of such geographically diverse audiences has sparked interest from many startups and social marketing gurus. However, formal study is lacking on both the timing and frequency problems. We study for the first time the broadcast scheduling problem of specifying the timing and frequency of publishing content to maximise the attention received. We validate and quantify three interacting behavioural phenomena to parametrise social platform users: information overload, bursty circadian rhythms and monotony aversion, which is defined here for the first time. We formalise a timeline information exchange process based on these phenomena, and formulate an objective function that quantifies the expected collective attention. We finally present experiments on real data from Twitter, where we discover a counter-intuitive scheduling strategy that outperforms popular heuristics while producing fewer posts.