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 a?? or patterns in general a?? 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 a?? at least within the social web's population a?? 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.
* Cover, Abstract, Acknowledgements, Table Of Contents | pdf
Chapter 1. Introduction | pdf
Chapter 2. Theoretical Background | pdf
Chapter 3. The Data - Characterisation, Collection and Processing | pdf
Chapter 4. First Steps on Event Detection in Large-Scale Textual Streams | pdfA
A A Note: Ch. 4 is an extended version of the paper "Tracking the flu pandemic by monitoring the Social Web"
Chapter 5. Nowcasting Events from the Social Web with Statistical Learning | pdfA
A A Note: Ch. 5 is an extended version of the paper "Nowcasting Events from the Social Web with Statistical Learning"A
Chapter 6. Detecting Temporal Mood Patterns by Analysis of Social Web Content | pdf
Chapter 7. Pattern Discovery Challenges in User Generated Web Content | pdf
Chapter 8. Theory in Practice - Applications Driven by our Theoretical Derivations | pdf
Chapter 9. Conclusions | pdf
* Appendices A & B | pdf
* Bibliography & Index | pdf