  Microblogs such as Twitter represent a powerful source of information. Part
of this information can be aggregated beyond the level of individual posts.
Some of this aggregated information is referring to events that could or should
be acted upon in the interest of e-governance, public safety, or other levels
of public interest. Moreover, a significant amount of this information, if
aggregated, could complement existing information networks in a non-trivial
way. This dissertation proposes a semi-automatic method for extracting
actionable information that serves this purpose. First, we show that predicting
time to event is possible for both in-domain and cross-domain scenarios.
Second, we suggest a method which facilitates the definition of relevance for
an analyst's context and the use of this definition to analyze new data.
Finally, we propose a method to integrate the machine learning based relevant
information classification method with a rule-based information classification
technique to classify microtexts. Fully automatizing microtext analysis has
been our goal since the first day of this research project. Our efforts in this
direction informed us about the extent this automation can be realized. We
mostly first developed an automated approach, then we extended and improved it
by integrating human intervention at various steps of the automated approach.
Our experience confirms previous work that states that a well-designed human
intervention or contribution in design, realization, or evaluation of an
information system either improves its performance or enables its realization.
As our studies and results directed us toward its necessity and value, we were
inspired from previous studies in designing human involvement and customized
our approaches to benefit from human input.
