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Topic Detection and Classification in Social Networks [electronic resource] : The Twitter Case / by Dimitrios Milioris.

By: Milioris, Dimitrios [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XVI, 105 p. 38 illus., 25 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319664149.Subject(s): Telecommunication | Computational intelligence | Image processing—Digital techniques | Computer vision | Computer engineering | Computer networks  | Natural language processing (Computer science) | Communications Engineering, Networks | Computational Intelligence | Computer Imaging, Vision, Pattern Recognition and Graphics | Computer Engineering and Networks | Natural Language Processing (NLP)Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
Introduction -- Background and Related Work -- Joint Sequence Complexity -- Text Classification via Compressive Sensing -- Extension of Joint Complexity and Compressive Sensing -- Conclusion.
In: Springer Nature eBookSummary: This book provides a novel method for topic detection and classification in social networks. The book addresses several research and technical challenges that are currently being investigated by the research community, from the analysis of relations and communications between members of a community, to quality, authority, relevance and timeliness of the content, traffic prediction based on media consumption, spam detection, to security, privacy and protection of personal information. Furthermore, the book discusses innovative techniques to address those challenges and provides novel solutions based on information theory, sequence analysis and combinatorics, which are applied on real data obtained from Twitter.
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Introduction -- Background and Related Work -- Joint Sequence Complexity -- Text Classification via Compressive Sensing -- Extension of Joint Complexity and Compressive Sensing -- Conclusion.

This book provides a novel method for topic detection and classification in social networks. The book addresses several research and technical challenges that are currently being investigated by the research community, from the analysis of relations and communications between members of a community, to quality, authority, relevance and timeliness of the content, traffic prediction based on media consumption, spam detection, to security, privacy and protection of personal information. Furthermore, the book discusses innovative techniques to address those challenges and provides novel solutions based on information theory, sequence analysis and combinatorics, which are applied on real data obtained from Twitter.

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