UC Riverside

In the last decade there has been a shift towards online

user-generated content. Services such Twitter, Facebook, Instagram or

Tumblr allow users to share their content and ideas in a simple

manner. The lower barrier of access (more non-technical users),

ubiquity and social capabilities generate the creation of content that

is user-centered: opinions, reviews, discussions and

interests. Effective and Efficient exploration and analysis of

user-generate content is an open and evolving question, which gained

increased interest in recent years.

The aim of my dissertation was to explore method and algorithms to

analyze and leverage the user-generated content. In particular our

objectives are as follows: 1) Study how we can leverage social network

data for prediction, recommendation or classification tasks; 2) Define

novel features that can be used to improve the efficiency/efficacy of

the models generated from user-content; and 3) Define tools and

algorithms that facilitate the use of user generated content.

This work has studied: how to collaboratively annotate user-generated

content in specific domains, how to define features that improve the

prediction accuracy of time series based on the social network

activity, how to define algorithms and measures to detect

spatio-temporal changes of topics, and how to improve accessibility to

the online social data, which is publicly available using a

restrictive API provided by the content provider.