In June 2016, there were an estimated 1.55 billion active users on the most popular social media platform, Facebook[1]. As of April 26th, 2017, there were an estimated 700 million active users on Instagram, our main social media platform for this research. As the number of users on social media platforms increases, a problem of great significance becomes clear: the increasing amount of negativi...

Location Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-temporal behavior. These social network provide a low rate sampling of user’s location information during large intervals of time that can be used to discover complex behaviors, including frequent routes, points of interest or unusual events. This information is important for different domains like...

BACKGROUND Prescription opioid misuse has doubled over the past 10 years and is now a public health epidemic. Analysis of social media data may provide additional insights into opioid misuse to supplement the traditional approaches of data collection (eg, self-report on surveys). OBJECTIVE The aim of this study was to characterize representations of codeine misuse through analysis of public p...

Social network sites are attracting the attention of numerous researchers. Recently, several studies have examined how people are forming identities on social networking sites. Therefore, greater levels of variable specification are required to further the research on this topic. Testing the relationships among key variables is important when trying to understand these online social forums. Alt...

The problem of sequential detection of anomalies in multimodal data is considered. The objective is to observe physical sensor data from CCTV cameras, and social media data from Twitter and Instagram to detect anomalous behaviors or events. Data from each modality is transformed to discrete time count data by using an artificial neural network to obtain counts of objects in CCTV images and by c...

Location Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-temporal behavior. These social network provide a low rate sampling of user’s location information during large intervals of time that can be used to discover complex behaviors, including mobility profiles, points of interest or unusual events. This information is important for different domains li...

The increased popularity and ubiquitous availability of online social networks and globalised Internet access have affected the way in which people share content. The information that users willingly disclose on these platforms can be used for various purposes, from building consumer models for advertising, to inferring personal, potentially invasive, information. In this work, we use Twitter, ...