Echo chambers, or circumstances in which one is only exposed to ideas that are similar to one’s own, are becoming a growing problem for political debate in many democratic countries. We explore political echo chambers on Twitter. We investigate the conditions that lead to the creation of echo chambers in online social networks by proposing a basic model of information sharing in online social networks. Furthermore, we investigate the role of “opposition” party users in the development of echo chambers, who consume their information with a varied leaning but generate partisan content (with a single-sided leaning). Finally, we use our findings to forecast supporters and opponents on the basis of social and cultural characteristics.

Recent research works have been focussing on estimating age from facial images. Age estimation from faces basically involves two sub-processes: extracting features and estimating learning function. Age classification from an input image is the task at hand in this project; age will be classified into 3 categories: 1) Toddler, 2) Teen, 3) Adult. Classifying age automatically from an image has been widely used in our day-to-day lives, particularly in the listed fields: biometrics, surveillance systems, and commercial kiosks. The purpose of this study is to categorize facial images based on their age. Prominently, previously existing research works were performed on contrived and unreal images curated in laboratories. Those images did not correctly portray the distinctions and fluctuations that are evident in real human faces. This paper uses deep convolutional neural networks (CNN) on the available data to overcome the above discussed challenge.