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.

Linguistic style matching (LSM) in conversations can be reflective of several aspects of social influence such as power or persuasion. However, how LSM relates to the outcomes of online communication on platforms such as Reddit is an unknown question. In this study, we analyze a large corpus of two-party conversation threads in Reddit where we identify all occurrences of LSM using two types of style: the use of function words and formality. Using this framework, we examine how levels of LSM differ in conversations depending on several social factors within Reddit: post and subreddit features, conversation depth, user tenure, and the controversiality of a comment. Finally, we measure the change of LSM following loss of status after community banning. Our findings reveal the interplay of LSM in Reddit conversations with several community metrics, suggesting the importance of understanding conversation engagement when understanding community dynamics.

We present POTATO, the Portable text annotation tool, a free, fully open-sourced annotation system that 1) supports labeling many types of text and multimodal data; 2) offers easy-to-configure features to maximize the productivity of both deployers and annotators (convenient templates for common ML/NLP tasks, active learning, keypress shortcuts, keyword highlights, tooltips); and 3) supports a high degree of customization (editable UI, inserting pre-screening questions, attention and qualification tests). Experiments over two annotation tasks suggest that POTATO improves labeling speed through its specially-designed productivity features, especially for long documents and complex tasks. POTATO is available at https://github.com/davidjurgens/potato and will continue to be updated.