Quantifying collective states from online social networking data

A significant fraction of humanity is now engaged in online social networking. Hundreds of millions of individuals publicly volunteer information on the most minute details of their experiences, thoughts, opinions, and feelings in real-time. This information is communicated largely in the form of very short texts, necessitating the development of text analysis algorithms that can determine psychological states from the content of sporadic and poorly formatted text updates. In this presentation I will provide an overview of the rapidly evolving domain of computational social science, which along with web science, is making significant contributions to our understanding of collective decision-making and social psychology. In our research we have developed tools to determine the collective states of online communities from large-scale social media data and have related these measurements to a variety of socio-economic indicators. We have shown how fluctuations of collective mood states match trends in the financial markets, used longitudinal data on the fluctuations of individual mood states to study mood homophily in social networks, and investigated how measures of online attention may yield new indicators of scholarly communication.