We analyze general trends and pattern in time series that characterize the dynamics of collective attention to social media services and Web-based businesses. Our study is based on search frequency data available from Google Trends and considers 175 different services. For each service, we collect data from 45 different countries as well as global averages. This way, we obtain more than 8,000 time series which we analyze using diffusion models from the economic sciences. We find that these models accurately characterize the empirical data and our analysis reveals that collective attention to social media grows and subsides in a highly regular and predictable manner. Regularities persist across regions, cultures, and topics and thus hint at general mechanisms that govern the adoption of Web-based services. We discuss several cases in detail to highlight interesting findings. Our methods are of economic interest as they may inform investment decisions and can help assessing at what stage of the general life-cycle a Web service is at.
The Social Web makes visible the ebb and flow of popular interest in topics both newsworthy ("GulfSpill") and trivial ("Lolcat"). Understanding this emergent behavior is a fundamental goal for Social Web research. Key problems include discovering emergent topics from online text sources, modeling burst activity, and predicting the future trajectory of a given topic. Past work has addressed such problems individually for specific applications, but has lacked a generalizable framework for performing both classification and prediction of topic usage. Our approach is to model a topic as a temporally ordered sequence of derived feature states and capture characteristic changes in the topic trend. These sequences are drawn from a dynamic segmentation of frequency data based on change point analysis. We employ Partitioning Around Medoids clustering on these segments to produce signatures which highlight characteristic patterns of usage growth and decay. We demonstrate how this signature model can be used to define distinctive classes of topics in multiple online contexts, including tagging systems and web-based information retrieval. Additionally, we show how the model can predict the general trajectory of interest in a particular topic. We study the relaxation response of a social system after endogenous and exogenous bursts of activity using the time series of daily views for nearly 5 million videos on YouTube. We find that most activity can be described accurately as a Poisson process. However, we also find hundreds of thousands of examples in which a burst of activity is followed by an ubiquitous power-law relaxation governing the timing of views. We find that these relaxation exponents cluster into three distinct classes and allow for the classification of collective human dynamics. This is consistent with an epidemic model on a social network containing two ingredients: a power-law distribution of waiting times between cause and action and an epidemic cascade of actions becoming the cause of future actions. This model is a conceptual extension of the fluctuation-dissipation theorem to social systems [Ruelle, D (2004) Phys Today 57:48–53] and [Roehner BM, et al, (2004) Int J Mod Phys C 15:809–834], and provides a unique framework for the investigation of timing in complex systems.