The viral diffusion of campaign messages about political issues during the 2016 U.S. presidential election

With candidates using social media sites like Facebook and Twitter as part of their campaign strategies, social scientists are trying to understand the diffusion of political messages. Viral events can spread messages fast and far from the source, bringing candidate’s messages to new audiences and bringing new followers to candidates. To date, no studies have focused on understanding specifically what kinds of political issues the public spreads into their own networks. While the kinds of issues that spread will likely change from election to election, this work provides a comparison point for future work and is the first step in more real-time analysis that could be useful for researchers, journalists, and politicians. For this poster abstract we highlight part of our analysis, specifically, the frequency with which presidential candidates tweeted about specific issues and how the public responded by retweeting. To accomplish this, we use data visualization for exploratory data analysis. We find that that candidates and the public are most interested in different topics, but that both the public and candidates are more interested in advocacy messages than attack messages for every topic. For the final poster we will present analysis of both Facebook and Twitter, as well as confirmatory statistical analysis using regression modeling

Studying the viral growth of a connective action network using information event signatures

The Arab spring and Occupy Wall Street movements demonstrated that networks of individuals who share interests or grievances could quickly form on social media. There is a reciprocal relationship between the growth of these networks and the information that flows through them. This study examines this relationship by using viral information event signatures, which show the changing rate of sharing of a specific message over a period of time. The Occupy movement and the digital interactions of its participants provides a context and rich corpus of data from which to study the relationship between the signatures of information flows and the growth the Occupy network. Using exploratory data analysis and multivariate regression to analyze Occupy related tweets drawn from a corpus of over 64 million tweets, this study first provides a parameterized signature model and then uses regression to show that a relationship exists between the shape of the signature and the rate at which key actors gain followers. This work also finds a quadratic decline, over the life cycle of the movement, in the rate at which the actors gain followers. The contributions of this work include the parameterized signature model, a demonstration of its usefulness, and a new perspective on the growth of the Occupy movement

Microcelebrity Practices: A Cross-Platform Study Through a Richness Framework

Social media have introduced a contemporary shift from broadcast to participatory media, which has brought about major changes to the celebrity management model. It is now common for celebrities to bypass traditional mass media and take control over their promotional discourse through the practice of microcelebrity. The theory of microcelebrity explains how people turn their public persona into media content with the goal of gaining and maintaining audiences who are regarded as an aggregated fan base. To accomplish this, the theory suggests that people employ a set of online self-presentation techniques that typically consist of three core practices: identity constructions, fan interactions and promoting visibility beyond the existing fan base. Studies on single platforms (e.g., Twitter), however, show that not all celebrities necessarily engage in all core practices to the same degree. Importantly, celebrities are increasingly using multiple social media platforms simultaneously to expand their audience, while overcoming the limitations of a particular platform. This points to a gap in the literature and calls for a cross-platform study. This dissertation employed a mixed-methods research design to reveal how social media platforms i.e., Twitter and Instagram, helped celebrities grow and maintain their audience. The first phase of the study relied on a richness scoring framework that quantified social media activities using affordance richness, a measure of the ability of a post to deliver the information necessary in affording a celebrity to perform an action by using social media artifacts. The analyses addressed several research questions regarding social media uses by different groups of celebrities and how the audience responded to different microcelebrity strategies. The findings informed the design of the follow-up interviews with audience members. Understanding expectations and behaviors of fans is relevant not only as a means to enhance the practice’s outcome and sustain promotional activity, but also as a contribution to our understandings about contemporary celebrity-fans relationships mediated by social media. Three findings are highlighted. First, I found that celebrities used the two platforms differently, and that different groups of celebrities emphasized different core practices. This finding was well explained by the interviews suggesting that the audiences had different expectations from different groups of celebrities. Second, microcelebrity strategies played an important role in an audience’s engagement decisions. The finding was supported by the interviews indicating that audience preferences were based on some core practices. Lastly, while their strategies had no effect on follow and unfollow decisions, the consistency of the practices had significant effects on the decisions. This study makes contributions to the theory of Microcelebrity and offers practical contributions by providing broad insights from both practitioners’ and audiences’ perspectives. This is essential given that microcelebrity is a learned practice rather than an inborn trait