Social media sites like Twitter enable users to engage in the spread of contagious phenomena: everything from information and rumors to social movements and virally marketed products. In particular, Twitter has been observed to function as a platform for political discourse, allowing political movements to spread their message and engage supporters, and also as a platform for information diffusion, allowing everyone from mass media to citizens to reach a wide audience with a critical piece of news. Previous work1 suggests that different contagious phenomena will display distinct propagation dynamics, and in particular that news will spread differently through a population than other phenomena. We leverage this theory to construct a system for classifying contagious phenomena based on the properties of their propagation dynamics, by combining temporal and network features. Our system, applicable to phenomena in any social media platform or genre, is applied to a dataset of news-related and political hashtags diffusing through the population of Russian Twitter users. Our results show that news-related hashtags have a distinctive pattern of propagation across the spectrum of Russian Twitter users. In contrast, we find that political hashtags display a number of different dynamic signatures corresponding to different politically active sub-communities. Analysis using ‘chronotopes’ sharpens these findings and reveals an important propagation pattern we call ‘resonant salience.’
