  A vast amount of textual web streams is influenced by events or phenomena
emerging in the real world. The social web forms an excellent modern paradigm,
where unstructured user generated content is published on a regular basis and
in most occasions is freely distributed. The present Ph.D. Thesis deals with
the problem of inferring information - or patterns in general - about events
emerging in real life based on the contents of this textual stream. We show
that it is possible to extract valuable information about social phenomena,
such as an epidemic or even rainfall rates, by automatic analysis of the
content published in Social Media, and in particular Twitter, using Statistical
Machine Learning methods. An important intermediate task regards the formation
and identification of features which characterise a target event; we select and
use those textual features in several linear, non-linear and hybrid inference
approaches achieving a significantly good performance in terms of the applied
loss function. By examining further this rich data set, we also propose methods
for extracting various types of mood signals revealing how affective norms - at
least within the social web's population - evolve during the day and how
significant events emerging in the real world are influencing them. Lastly, we
present some preliminary findings showing several spatiotemporal
characteristics of this textual information as well as the potential of using
it to tackle tasks such as the prediction of voting intentions.
