Variable Attention and Variable Noise: Forecasting User Activity

The study of collective attention is of growing interest in an age where massand social media generate massive amounts of often short lived information. That is, the problem of understanding how particular ideas, news items, or memes grow and decline in popularity has become a central problem of the information age. Recent research efforts in this regard have mainly addressed methods and models which quantify the success of such memes and track their behavior over time. Surprisingly, however, the aggregate behavior of users over various news and social media platforms where this content originates has large been ignored even though the success of memes and messages is linked to the way users interact with web platforms. In this paper, we therefore present a novel framework that allows for studying the shifts of attention of whole populations related to websites or blogs. The framework is an extension of the Gaussian process methodology, where we incorporate regularization methods that improve prediction and model input dependent noise. We provide comparisons with traditional Gaussian process and show improved results. Our study in a real world data set, uncovers hidden patterns of user behavior.