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Online Social Networks Event Detection: A Survey

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Solving Large Scale Learning Tasks. Challenges and Algorithms

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9580))

Abstract

Today online social network services are challenging state-of-the-art social media mining algorithms and techniques due to its real-time nature, scale and amount of unstructured data generated. The continuous interactions between online social network participants generate streams of unbounded text content and evolutionary network structures within the social streams that make classical text mining and network analysis techniques obsolete and not suitable to deal with such new challenges. Performing event detection on online social networks is no exception, state-of-the-art algorithms rely on text mining techniques applied to pre-known datasets that are being processed with no restrictions on the computational complexity and required execution time per document analysis. Moreover, network analysis algorithms used to extract knowledge from users relations and interactions were not designed to handle evolutionary networks of such order of magnitude in terms of the number of nodes and edges. This specific problem of event detection becomes even more serious due to the real-time nature of online social networks. New or unforeseen events need to be identified and tracked on a real-time basis providing accurate results as quick as possible. It makes no sense to have an algorithm that provides detected event results a few hours after being announced by traditional newswire.

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Notes

  1. 1.

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Acknowledgements

This work was supported by national funds, through the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT), and by European Commission through the project MAESTRA (Grant number ICT-2013-612944).

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  1. University of Porto, Porto, Portugal

    Mário Cordeiro

  2. INESC TEC Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal

    João Gama

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  1. Mário Cordeiro
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Correspondence to Mário Cordeiro .

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  1. TU Dortmund , Dortmund, Germany

    Stefan Michaelis

  2. TU Dortmund , Dortmund, Germany

    Nico Piatkowski

  3. TU Dortmund , Dortmund, Germany

    Marco Stolpe

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Cordeiro, M., Gama, J. (2016). Online Social Networks Event Detection: A Survey. In: Michaelis, S., Piatkowski, N., Stolpe, M. (eds) Solving Large Scale Learning Tasks. Challenges and Algorithms. Lecture Notes in Computer Science(), vol 9580. Springer, Cham. https://doi.org/10.1007/978-3-319-41706-6_1

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