• by Erik Sass , Staff Writer, December 26, 2014

Online social networks can play a major role influencing decisions by individuals, and the timing of social media content is key when it comes to marketers disseminating marketing messages, according to a new study by researchers from Cornell University and Sapienza University of Rome, titled “How to Schedule a Cascade in an Arbitrary Graph” and published in the SIAM Journal on Computing.

The researchers analyzed data from social networks and developed an algorithm designed to account for the mechanics of personal decision-making, meaning how an individual's attitudes and choices are affected by the prevalence of attitudes among their peers, as well as the element of timing, or how scheduling of campaigns affects their subsequent spread -- or failure to spread -- across social media. Ultimately, the goal was to determine how to produce social “cascades” in which the behaviors of large numbers of people became coordinated over time.

Co-author Jon Kleinberg of Cornell University explained the role played by timing: “To our surprise, the success of the cascade can sometimes be greatly affected by this choice of timing -- with the right timing strategy, the cascade can have a good likelihood of spreading very widely, while with the wrong strategy, it can have very little chance of going far. The mathematical and computational challenge for us was then to characterize the kinds of timing strategies that are most effective, and how these strategies depend on the structure of the network in which they are operating.”Kleinberg cited several potential real-world applications for the algorithm, “for example, how a company can choose to roll out a product at different times in different geographic areas or to different markets.”

Earlier this year I wrote about a study by researchers at Yale, the University of California-San Diego, the Universidad Autónoma of Madrid, and NICTA of Australia, who formulated a technique that they claim can forecast social media trends up to two months in advance. The study, described in a paper titled “Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks”  and published in the online journal PLoS One, focused on the role of “sensors,” meaning central individuals with more social connections who act as sentinels for wider networks, registering an emergent trend before it becomes prevalent across the network and the Internet at large. One advantage of the system is it allows researchers to predict trends based on a relatively small sample size using local monitoring,” rather than trying to wrangle data from the entire social media universe.

Last year a study by researchers at the U.S. Military Academy at West Point's Network Science Center, titled “A Scalable Heuristic for Viral Marketing Under the Tipping Model,” examined 36 social networks (including nine academic collaboration networks, three e-mail networks, and 24 networks extracted from social media sites) to deduce an approach for identifying “seed groups” -- the subsets of larger groups who are most likely to propagate a viral marketing message.