Measuring Propagation Phenomena in Social Networks: Promising Directions and Open Issues

Abstract

The massive information is now spreading like wildfire in social media. As the usage of social data increased, the abuse of the media to spread distorted data also increased several times. To understand and predict the spread of information over a time period in online social networks researchers attempt to quantitatively model and measure the whole process. A number of different statistics aimed at measuring the spread were suggested. Many researchers have coupled these measures with various forgetting factor mechanisms to improve behavioural properties. Unfortunately, frequent unavailability of the full data record in social media prevents straightforward validation of such quantities. Moreover, since most known measures have global affects, they are rather inconvenient to evaluate for large networks. These difficulties lead us to contribute here a methodological identification of the propagation parameters to start afresh. The approach hinges on some recent results arising from the convergence between threshold models and cascade models. For example, three key concepts – distance, centrality and robustness – is successfully balanced by the proposed scope–speed–failures relationship. We conclude by identifying several open issues and possible directions for future research.