The focus of this research project is understanding how information propagates through complex socio-technical information networks. Leveraging large-scale public data from online social networking platforms, we are able to analyze and model the spread of information, from news to social movements, from political discourse to market trends, and from trending topics to scientific results, in unprecedented detail.
One goal of this project is to study how social network structure, finite attention, popular sentiment, user influence, and other factors affect the manner in which information is disseminated. A second goal is to better understand how social media can be abused, for example by malicious social bots, astroturf, orchestrated campaigns, and online hoaxes.
Our work to date includes a number of core research themes:
- Theoretical models and empirical analyses to better understand how information spreads, how the structure of social networks can help predict which memes are likely to become viral, the role of limited attention and sentiment on the diffusion process, and the mutual interaction between traffic on the network and the emergent structure of the network.
- Computational social science methods exploring the correlations between online and offline events. Examples of research to date include analyses of geographic and temporal patterns in movements like Occupy Wall Street, societal unrest in Turkey, polarization and cross-ideological communication in online discourse, partisan asymmetries in political engagement, the use of social media data to predict election outcomes and forecast key market indicators, and the geographic diffusion of trending topics.
- Development of an Observatory to share and explore data derived from our meme diffusion analytics, making this data more easily accessible and thus more useful to social scientists, reporters, and the general public. Deployment of machine learning algorithms to help classify content and its producers. Applications include social bot detection, an API for exploring historical Twitter data, and visualizations of temporal, geographic, and network patterns.