Replication Data for: Bail, Christopher A. 2016. "Combining Network Analysis and Natural Language Processing to Examine how Advocacy Organizations Stimulate Conversation on Social Media." Proceedings of the National Academy of Sciences, 113:42 11823-11828

These data describe the social media outreach of 82 autism and organ donation advocacy organizations on Facebook between 2011-2012 collected via a social media application that collected public and non-public data from the Facebook Application Programming Interface and surveyed representatives of the organization in order to obtain additional information on the size and resources of the organization as it attempts to generate attention for its cause on social media. The dataset also includes a variable created via a combination of natural language processing and social network analysis that are designed to explain why the messages of some advocacy organizations stimulate more public attention than others.

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ABSTRACT (of article)

Social media sites are rapidly becoming one of the most important forums for public deliberation about advocacy issues. However, social scientists have not explained why some advocacy organizations produce social media messages that inspire far-ranging conversation among social media users, whereas the vast majority of them receive little or no attention. I argue that advocacy organizations are more likely to inspire comments from new social media audiences if they create “cultural bridges,” or produce messages that combine conversational themes within an advocacy field that are seldom discussed together. I use natural language processing, network analysis, and a social media application to analyze how cultural bridges shaped public discourse about autism spectrum disorders on Facebook over the course of 1.5 years, controlling for various characteristics of advocacy organizations, their social media audiences, and the broader social context in which they interact. I show that organizations that create substantial cultural bridges provoke 2.52 times more comments about their messages from new social media users than those that do not, controlling for these factors. This study thus offers a theory of cultural messaging and public deliberation and computational techniques for text analysis and application-based survey research.