Citizen Sensor Data Mining, Social Media Analytics and Applications

Document Type

Presentation

Abstract

With the rapid rise in the popularity of social media (1B+ Facebook users, 200M+ twitter users), and near ubiquitous mobile access (4+ billion actively-used mobile phones), the sharing of observations and opinions has become common-place (500M+ tweets a day). 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 for brand tracking and management, crisis coordination, organizing revolutions or promoting social development in underdeveloped and developing countries. I will review: 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) how we built Twitris, a comprehensive social media analytics (social intelligence) platform. I will describe the analysis capabilities along three dimensions: spatio-temporal-thematic, people-content-network, and sentiment-emption-intent. I will couple technical insights with identification of computational techniques and real-world examples using live demos of Twitris.

APA Citation

Sheth, A. P. (2015). Citizen Sensor Data Mining, Social Media Analytics and Applications.
https://corescholar.libraries.wright.edu/knoesis/1058

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