Labeling large scale social media data using budget-driven One-class SVM classification

The social media classification problems draw more and more attention in the past few years. With the rapid development of Internet and the popularity of computers, there is astronomical amount of information in the social network (social media platforms). The datasets are generally large scale and are often corrupted by noise. The presence of noise in training set has strong impact on the performance of supervised learning (classification) techniques. A budget-driven One-class SVM approach is presented in this thesis that is suitable for large scale social media data classification. Our approach is based on an existing online One-class SVM learning algorithm, referred as STOCS (Self-Tuning One-Class SVM) algorithm. To justify our choice, we first analyze the noise-resilient ability of STOCS using synthetic data. The experiments suggest that STOCS is more robust against label noise than several other existing approaches. Next, to handle big data classification problem for social media data, we introduce several budget driven features, which allow the algorithm to be trained within limited time and under limited memory requirement. Besides, the resulting algorithm can be easily adapted to changes in dynamic data with minimal computational cost. Compared with two state-of-the-art approaches, Lib-Linear and kNN, our approach is shown to be competitive with lower requirements of memory and time.

Electronic Technology in NL Schools: An Environmental Scan, 2015-16

This study was designed to obtain information on the prevalence of electronic technology—in terms of availability and use—in classrooms in Newfoundland and Labrador. An online survey was developed and delivered electronically to a randomly chosen sample of 800 k-12 educators in Newfoundland & Labrador’s English School District during Winter, 2016. In total, 377 surveys were completed. Among other things, the findings showed that SMART Boards and iPads were receiving significant usage while the usage of computer labs and of various social media tools was not particularly high.