  A number of recent studies of information diffusion in social media, both
empirical and theoretical, have been inspired by viral propagation models
derived from epidemiology. These studies model the propagation of memes, i.e.,
pieces of information, between users in a social network similarly to the way
diseases spread in human society. Importantly, one would expect a meme to
spread in a social network amongst the people who are interested in the topic
of that meme. Yet, the importance of topicality for information diffusion has
been less explored in the literature.
  Here, we study empirical data about two different types of memes (hashtags
and URLs) spreading through the Twitter's online social network. For every
meme, we infer its topics and for every user, we infer her topical interests.
To analyze the impact of such topics on the propagation of memes, we introduce
a novel theoretical framework of information diffusion. Our analysis identifies
two distinct mechanisms, namely topical and non-topical, of information
diffusion. The non-topical information diffusion resembles disease spreading as
in simple contagion. In contrast, the topical information diffusion happens
between users who are topically aligned with the information and has
characteristics of complex contagion. Non-topical memes spread broadly among
all users and end up being relatively popular. Topical memes spread narrowly
among users who have interests topically aligned with them and are diffused
more readily after multiple exposures. Our results show that the topicality of
memes and users' interests are essential for understanding and predicting
information diffusion.
