Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications

With the rapid rise in the popularity of social media (500M+ Facebook users, 100M+ twitter users), and near ubiquitous mobile access (4.1 billion actively-used mobile phones), the sharing of observations and opinions has become common-place (nearly 100M tweets a day, 1.8 trillion SMSs in US last year). This has given us an unprecedented access to the pulse of a populace and the ability to perform analytics on social data to support a variety of socially intelligent applications -- be it towards targeted online content delivery, crisis management, organizing revolutions or promoting social development in underdeveloped and developing countries. This tutorial will address challenges and techniques for building applications that support a broad variety of users and types of social media. This tutorial will focus on social intelligence applications for social development, and cover the following research efforts in sufficient depth: 1) understanding and analysis of informal text, esp. microblogs (e.g., issues of cultural entity extraction and role of semantic/background knowledge enhanced techniques), and 2) building social media analytics platforms. Technical insights will be coupled with identification of computational techniques and real-world examples

Дослідження ефективності застосування технології Machine Learning Services в задачах прогнозування

Викладено і проаналізовано результати експериметів щодо ефективності розв’язання задач прогнозування методами Machine Learning із застосуванням технології Machine Learning Services. Ця технологія полягає у перенесенні процесів оброблення даних з комп’ютера клієнта (як це реалізовано у класичній технології Machine Learning) на сервер, на якому зберігаються дані. Дослідження проводилися шляхом порівняння витрат часу розв’язання задач за кожною технологією при різних обсягах даних. Результати досліджень показали, що застосування технології Machine Learning Services має у два рази кращі показники на кількості даних понад півтора мільйона записів

Multimodal Social Intelligence in a Real-Time Dashboard System

Social Networks provide one of the most rapidly evolving data sets in existence today. Traditional Business Intelligence applications struggle to take advantage of such data sets in a timely manner. The BBC SoundIndex, developed by the authors and others, enabled real-time analytics of music popularity using data from a variety of Social Networks. We present this system as a grounding example of how to overcome the challenges of working with this data from social networks. We discuss a variety of technologies to implement near real-time data analytics to transform Social Intelligence into Business Intelligence and evaluate their effectiveness in the music domain. The SoundIndex project helped to highlight a number of key research areas, including named entity recognition and sentiment analysis in Informal English. It also drew attention to the importance of metadata aggregation in multimodal environments. We explored challenges such as drawing data from a wide set of sources spanning a myriad of modalities, developing adjudication techniques to harmonize inputs, and performing deep analytics on extremely challenging Informal English snippets. Ultimately, we seek to provide guidance on developing applications in a variety of domains that allow an analyst to rapidly grasp the evolution in the social landscape, and show how to validate such a system for a real-world application