A paper reporting some results from a new study by Social Intelligence Lab researcher Yoav Achiam, and co-authored by Inbal Yahav and David Schwartz, has been accepted for presentation at the forthcoming IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining to be held in San Francisco.
This work discusses simulation of Companies’ ego networks on Twitter, that is the companies’ number and type of followers. Evident from data, the study shows that followers distribution, is neither scale free nor random, thus common network simulations cannot be used to mimic observed data. A novel rate equations model to capture the complex dynamics of these ego networks is presented. By defining ego networks on dimensions that more accurately characterize microblog networks such as Twitter: quantity, quality, and timeliness, we have been able to generate a simulation model that captures Twitter dynamics better than existing baselines. Following data analysis that explained the lack of a scale free distribution we defined a new ego network rate equations-based simulation model. Our experiments and simulation show a resulting fit to accepted models indicating that this new model can be effectively applied for company ego network analysis.