Extracting actionable information from microtexts
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This dissertation focuses on extracting actionable information from microtexts, such as tweets, by developing semi-automated methods that combine machine learning and rule-based techniques.
Three key contributions are: predicting the timing of events, facilitating the definition of relevance for analysts, and integrating machine learning with information classification to categorize microtexts. The work emphasizes the role of human intervention in improving the effectiveness of automated systems, especially in crisis scenarios.
Hürriyetoğlu, A. (2019). Extracting actionable information from microtexts. Dissertation, Radboud University Nijmegen, handle:2066/204517.