Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data

Mining human mobility data and social media for smart services

Learning with Large - Scale Social Media Networks

Social media such as blogs, Facebook, Twitter, YouTube and Flickr enables people of all walks of life to express their thoughts, voice their opinions, and connect to each other more conveniently than ever. The boom of social media opens up a vast range of possibilities to study human interactions and collective behavior on an unprecedented scale. This dissertation presents a framework for learn...

Large-Scale Sleep Condition Analysis Using Selfies from Social Media

Sleep condition is closely related to an individual’s health. Poor sleep conditions such as sleep disorder and sleep deprivation affect one's daily performance, and may also cause many chronic diseases. Many efforts have been devoted to monitoring people's sleep conditions. However, traditional methodologies require sophisticated equipment and consume a significant amount of time. In this paper...

Activity Discovery Using Large-Scale Geo-Tagged Social Media

Understanding human activities is gradually recognized as a cornerstone task that is critical to a large number of practical applications, ranging from urban planning to location recommendation. While traditional approaches of analyzing people’s city-wide activities heavily rely on paid surveys and human investigations , the explosively growing geo-tagged social media (GSM) brings new opportuni...

Big Data for Social Sciences: Measuring patterns of human behavior through large-scale mobile phone data

The present study provides the first evidence that illiteracy can be reliablypredicted from standard mobile phone logs. By deriving a broad set of mobile phoneindicators reflecting users’ financial, social and mobility patterns we show howsupervised machine learning can be used to predict individual illiteracy in an Asiandeveloping country, externally validated against a large-s...