computational social science

Research at the intersection of machine learning and the social sciences has provided critical new insights into social behavior. At the same time, a variety of issues have been identified with the machine learning models used to analyze social data.

The present work proposes the use of social media as a tool for better understanding the relationship between a journalists' social network and the content they produce. Specifically, we ask: what is the relationship between the idealogical leaning of a journalist's social network on Twitter and the news content he or she produces?

Can data from mobile phones be used to observe economic shocks and their consequences at multiple scales? Here we present novel methods to detect mass layoffs, identify individuals affected by them and predict changes in aggregate unemployment rates using call detail records (CDRs) from mobile phones.

Social media have been employed to assess public opinions on events, markets, and policies. Most current work focuses on either developing aggregated measures or opinion extraction methods like sentiment analysis. These approaches suffer from unpredictable turnover in the participants and the information they react to, making it difficult to distinguish meaningful shifts from those that follow from known information.