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The rise of big data, especially social media data (e.g., Twitter, Facebook, Youtube), gives new
opportunities to the understanding of human behavior. Consequently, novel computing methods
for mining patterns in social media data are therefore desired. Through applying these approaches,
it has become possible to aggregate public available data to capture triggers underlying events,
detect on-going trends, and forecast future happenings.
This thesis focuses on developing methods for social media analysis. Specifically, five directions
are proposed here: 1) semi-supervised detection for targeted-domain events, 2) topical interaction
study among multiple datasets, 3) discriminative learning about the identifications for common
and distinctive topics, 4) epidemics modeling for flu forecasting with simulation via signals from
social media data, 5) storyline generation for massive unorganized documents.