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.

Implementation of Marine Protected Areas as a governability challenge in Southern Brazil

Marine Protected Areas (MPAs) have been endorsed internationally as a tool promoting marine conservation. MPAs implementation can however be challenging, with many not achieving conservation objectives while creating conflicts with small-scale fishers, often overlooked. This study aims at exploring how MPA implementation can be improved, taking as a case study the Marine National Park of Currais Islands in Southern Brazil. Specifically, this thesis offers a theoretical contribution by applying the interactive governance framework to analyze both the natural and the social systems, focusing on small-scale fishers, governed by the MPA as an institution. The thesis also presents a methodological contribution, as it develops and applies an exploratory mapping approach. We surveyed 65 small-scale fishers from eight communities at Pontal do Paraná municipality, and conducted an exploratory mapping approach in small groups. Main findings point to challenges and opportunities to MPA implementation, and offer a way to involve small-scale fishers in the discussion about MPAs.