  Although the spread of behaviors is influenced by many social factors,
existing literature tends to study the effects of single factors -- most often,
properties of the social network -- on the final cascade. In order to move
towards a more integrated view of cascades, this paper offers the first
comprehensive investigation into the role of two social factors in the
diffusion of 1,337 popular hashtags representing the production of novel
culture on Twitter: 1) the topology of the Twitter social network and 2)
performance of each user's probable demographic identity. Here, we show that
cascades are best modeled using a combination of network and identity, rather
than either factor alone. This combined model best reproduces a composite index
of ten cascade properties across all 1,337 hashtags. However, there is
important heterogeneity in what social factors are required to reproduce
different properties of hashtag cascades. For instance, while a combined
network+identity model best predicts the popularity of cascades, a network-only
model has better performance in predicting cascade growth and an identity-only
model in adopter composition. We are able to predict what type of hashtag is
best modeled by each combination of features and use this to further improve
performance. Additionally, consistent with prior literature on the combined
network+identity model most outperforms the single-factor counterfactuals among
hashtags used for expressing racial or regional identity, stance-taking,
talking about sports, or variants of existing cultural trends with very slow-
or fast-growing communicative need. In sum, our results imply the utility of
multi-factor models in predicting cascades, in order to account for the varied
ways in which network, identity, and other social factors play a role in the
diffusion of hashtags on Twitter.
