The study of social and political networks is a very active line of research within the new discipline of Computational Social Science. The ubiquity of social media, the possibility of collecting large-scale social behavior data from its users, as well as its impact on various critical phenomena such as voting, social movements, and health-related activities, have all sparked much interest in theories and methods of network science and complex systems.
Many interdisciplinary projects involving numerous distinct labs associated with IUNI currently focus on leveraging massive data from social media and the web to study the structure, content, impact, and dynamic behavior of networks of individuals, communities, and institutions. For example, several graduate students are involved in modeling the diffusion of information online to understand the mechanisms driving the spread of memes on social networks such as Twitter. Using big data analytics, complex networks, and agent-based models, this work is shedding light on how competition for finite attention affects what information we propagate, what social connections we make, and how the structure of social and topical networks can help predict which memes are likely to become viral.
Another research topic concerns the study of politically inspired social phenomena (e.g., protests, political polarization) via the analysis of online discourse (Razo, Menczer, Flammini, Bollen, Rocha, Rojas). Our students have analyzed geographic and temporal patterns in movements like Occupy Wall Street, societal unrest, the polarization of political discourse both online and in deliberative bodies, and the prediction of election outcomes. A more applied effort is directed at developing tools to help the public understand how easily online media can be manipulated and how such abuse can be mitigated (Menczer, Flammini, Rocha, Bollen). Our students were the first to uncover systematic attempts to mislead the public on a large scale through social bots, rumor spreading, and orchestrated persuasion campaigns, as well as to introduce computational methods to detect these abuses and to explore automatic fact-checking. A social media observatory infrastructure is being deployed to support this research, which is expected to enhance interdisciplinary collaborations with social scientists, particularly to test theories given the large new data from social media. Most recently, our research has focused on the cognitive, social, and technological factors that affect the spread of misinformation, including fake news, in social media. Work by our students in these topics is grabbing many headlines. Other active research threads include the geographic diffusion of trending topics; emotional contagion; evolution of beliefs; social media data for public-health monitoring (See Figure) and to predict phenomena such as stock market movements and fashion model success.
In addition to social media and big data analysis, students gain exposure to new lines of network science research such as the modeling of strategic formation of networks, which extends social media analysis by questioning the incentives for people to form ties for particular purposes. Other longstanding social science research questions becoming more readily analyzed due to data and network-analytic approaches include:
- Why do nations establish political and economic agreements or, alternatively, go to war with one another?
- Why do citizens and private organizations establish transnational ties?
- How do groups create social capital?
- How do people form political beliefs and attitudes?
- How do citizens and organizations rely on connections to navigate social and political environments?
- How do businesses and governments interact?
- How to predict price, risk and technology adoption dynamics in economics and finance?