To help users better understand the potential risks associated with publishing data publicly, and the types of data that can be inferred by combining data from multiple online sources, we introduce a novel information exposure detection framework that generates and analyzes the web footprints users leave across the social web. ...

This paper presents a framework for identifying persistent groups and individuals across multiple time granularities in dynamic graphs. Understanding the longevity of groups and the relevance of individuals within a group is important in many fields, including sociology, biology, economics, psychology, and political science. Different clustering algorithms have been proposed ...

Traditional public health surveillance requires regular clinical reports and considerable effort by health professionals to analyze data. Therefore, a low cost alternative is of great practical use. As a platform used by over 500 million users worldwide to publish their ideas about many topics, including health conditions, Twitter provides researchers ...