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.
Speaker(s)
Nemanja Spasojevic
Nemanja Spasojevic is the director of data science at Lithium Technologies. He graduated from Massachusetts Institute of Technology and previously worked on the Google Books project, making all of the world’s knowledge accessible online.
Adithya Rao
Adithya Rao is the lead research engineer on the data science team at Lithium Technologies. He graduated with a master’s degree from Stanford University, and his interests include machine learning, data mining, and information retrieval.