We address the problem of detecting and recognizing the text embedded in online images that are circulated over the Web. Our idea is to leverage context information for both text detection and recognition. For detection, we use local image context around the text region, based on that the text often sequentially appear in online images. For recognition, we exploit the metadata associated with t...

Recent research has identified a relatively new trend among youth (12e24) living in violent urban neighborhoods. These youth use social networking sites like Facebook, Twitter and Instagram to brag about violence, make threats, recruit gang members and to plan criminal activity known as Internet banging. Studies have typically examined youth communication by mining data on social media and surv...

Understanding the usage of multiple Online Social Networks (OSNs) is of significant research interest as it helps in identifying the distinguishing traits of each social media platform that contribute to its continued existence. A comparison between two OSNs is particularly useful when it is done on the representative set of users holding active accounts on both the platforms. In this research,...

Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Inspired by a blog post [1], we tried to predict the probability of an image getting a high number of likes on Instagram. We modified a pre-trained AlexNet ImageNet CNN model using Caffe on a new dataset of Instagram images with hashtag ‘me’ to predict the likability of photo...

The challenge of associating entities across multiple domains is a key problem in social media understanding. Successful cross-domain entity resolution provides integration of information from multiple sites to create a complete picture of user and community activities, characteristics, and trends. In this work, we examine the problem of entity resolution across Twitter and Instagram using gene...

We introduce Instagram as a data source for use by scholars in urban studies and neighboring disciplines and propose ways to operationalize key concepts in the study of cities. These data can help shed light on segregation, the formation of subcultures, strategies of distinction, and status hierarchies in the city. Drawing on two datasets of geotagged Instagram posts from Amsterdam and Copenhag...

This paper examines recent acclaim for professional embedded photojournalists who visually document the experience of US soldiers in Afghanistan using the popular mobile photo application Hipstamatic. These photos have stirred controversy among fellow journalists and cultural critics regarding the use of photo filters in Hipstamatic and similar app Instagram, their contribution to the de-profes...

With the development in technology and the increasing ubiquity of social media services, it has created new opportunities to study personality from the digital traces individuals leave behind. The large number of user-generated images on social media has prompted renewed interests in understanding the psychological factors driving production and consumption behaviours of visual content. Instagr...

Emojis are small images that are commonly included in social media text messages. The combination of visual and textual content in the same message builds up a modern way of communication, that automatic systems are not used to deal with. In this paper we extend recent advances in emoji prediction by putting forward a multimodal approach that is able to predict emojis in Instagram posts. Instag...

Cyberbullying is a growing problem affecting more than half of all American teens. The main goal of this paper is to investigate fundamentally new approaches to understand and automatically detect incidents of cyberbullying over images in Instagram, a media-based mobile social network. To this end, we have collected a sample Instagram data set consisting of images and their associated comments,...