When-To-Post on Social Networks

TL;DR

This paper addresses the challenge of recommending optimal posting times on social networks to maximize audience reactions, analyzing user behavior across platforms and locations, and proposing personalized scheduling methods validated on large-scale data.

Contribution

It formulates the when-to-post problem, analyzes user reaction patterns, and develops personalized scheduling approaches validated on extensive real-world data.

Findings

Up to 17% reaction increase on Facebook using recommended times

Up to 4% reaction increase on Twitter with scheduling

Validated on over 0.5 million users and 25 million messages

Abstract

For many users on social networks, one of the goals when broadcasting content is to reach a large audience. The probability of receiving reactions to a message differs for each user and depends on various factors, such as location, daily and weekly behavior patterns and the visibility of the message. While previous work has focused on overall network dynamics and message flow cascades, the problem of recommending personalized posting times has remained an underexplored topic of research. In this study, we formulate a when-to-post problem, where the objective is to find the best times for a user to post on social networks in order to maximize the probability of audience responses. To understand the complexity of the problem, we examine user behavior in terms of post-to-reaction times, and compare cross-network and cross-city weekly reaction behavior for users in different cities, on both Twitter and Facebook. We perform this analysis on over a billion posted messages and observed reactions, and propose multiple approaches for generating personalized posting schedules. We empirically assess these schedules on a sampled user set of 0.5 million active users and more than 25 million messages observed over a 56 day period. We show that users see a reaction gain of up to 17% on Facebook and 4% on Twitter when the recommended posting times are used. We open the dataset used in this study, which includes timestamps for over 144 million posts and over 1.1 billion reactions. The personalized schedules derived here are used in a fully deployed production system to recommend posting times for millions of users every day.