Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media
Significance Social media provide potent opportunities for advocacy organizations to shape public debates because of the rapidly increasing number of people who frequent such forums each day. However, social scientists have not yet explained why some advocacy organizations create large scale public debate whereas most others do not. Using automated text analysis and a Facebook application, I found that advocacy organizations are more likely to stimulate conversation if they produce messages that link discursive themes that are usually discussed in isolation from each other. Such messages not only resonate with multiple audiences, but also put such audiences in conversation with each other. This manuscript thereby contributes a theory of public deliberation on social media for the emerging field of computational social science. 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.
Proceedings of the National Academy of Sciences