BIDS Data Science Lecture Series | October 9, 2015 | 1:00-2:30 p.m. | 190 Doe Library, UC Berkeley
Sponsors: Berkeley Institute for Data Science and the Data, Society and Inference Seminar
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. 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. This study consists of user behavior analysis as well as a proposed scheme for calculating personalized schedules.