Effective and Efficient Analysis on Internet-based Social Networks
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.